how to become a quant without phd

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• What Do Quant Traders Really Do?

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4 Steps to Becoming a Quant Trader

Pete Rathburn is a copy editor and fact-checker with expertise in economics and personal finance and over twenty years of experience in the classroom.

Lucrative salaries, hefty bonuses, and creativity on the job have resulted in  quantitative trading  becoming an attractive career option. Quantitative traders, or quants for short, use mathematical models to identify trading opportunities and buy and sell securities. The influx of candidates from academia, software development, and engineering has made the field quite competitive. In this article, we’ll look more closely at what quants do and the skills and education you need to become one.

Key Takeaways

• Quant traders use strategies based on quantitative analysis—mathematical computations and number crunching—to find trading possibilities that can involve hundreds of thousands of securities.
• An aspiring quant trader needs to be exceptionally skilled and interested in all things mathematical—if you don't live, breathe, and sleep numbers, then this is not the field for you.
• A bachelor's degree in math and a master's degree in financial engineering or quantitative financial modeling or an MBA are all helpful for scoring a job; some analysts will also have a Ph.D. in these or similar fields.
• Lacking an advanced degree, a candidate should at least have on-the-job training and experience as a data analyst; experience with data mining, research, analysis, and automated trading systems are a must.
• Traders also need soft skills, such as the ability to thrive under pressure, maintain focus despite long hours, withstand an intense, aggressive environment, and stomach setbacks and failures in pursuit of success.

What Do Quant Traders Really Do?

The word "quant" is derived from quantitative, which essentially means working with numbers. The advancement of computer-aided  algorithmic trading  and high-frequency trading means there is a huge amount of data to be analyzed. Quants mine and research the available price and quote data, identify profitable trading opportunities, develop relevant  trading strategies , and capitalize on opportunities with lightning-fast speed using self-developed computer programs.

Quant traders must be exceptionally good with mathematics and quantitative analysis. For example, if terms like conditional probability, skewness, kurtosis, and VaR don’t sound familiar, then you’re probably not ready to be a quant. In-depth knowledge of math is a must for researching data, testing the results, and implementing identified trade strategies. Identified trade strategies, implemented algorithms, and trade execution methods should be as fool-proof as possible. In the present day, lightning-fast trading world, complex number-crunching trading algorithms occupy a majority of the market share. Even a small mistake in the underlying concept on the part of the quant trader can result in a huge trading loss.

In essence, a quant trader needs a balanced mix of in-depth mathematics knowledge, practical trading exposure, and computer skills. Below are the steps to landing a job as a quant trader.

Quant traders can work for investment firms, hedge funds, and banks, or they can be proprietary traders, using their own money for investment. 

It is usually difficult for new college graduates to score a job as a quant trader. A more typical career path is starting out as a data research analyst and becoming a quant after a few years. Education like a master's degree in financial engineering, a diploma in quantitative financial modeling, or electives in quantitative streams during the regular MBA may give candidates a head start. These courses cover the theoretical concepts and practical introduction to tools required for quant trading.

An aspiring quant should have, at minimum, a background in finance, mathematics, and computer programming. In addition, quants should have the following skills and background: 

• Trading concepts :   Quants are expected to discover and design their own unique trading strategies and models from scratch as well as customize established models. A quant trading candidate should have a detailed knowledge of popular trading strategies as well as each one's respective advantages and disadvantages.
• Programming skills :   Quant traders must be familiar with data mining, research, analysis, and automated trading systems. They are often involved in high-frequency trading or algorithmic trading. A good understanding of at least one programming language is a must, and the more programs the candidate knows, the better. C++, Java, Python, and Perl are a few commonly used programming languages. Familiarity with tools like MATLAB and spreadsheets, and concepts like big data and data structuring, is a plus. 
• Computer usage :   Quants implement their own algorithms on real-time data containing prices and quotes. They need to be familiar with any associated systems, like a Bloomberg terminal, which provides data feeds and content. They should also be comfortable with charting and analysis software applications and spreadsheets and be able to use broker trading platforms to place orders. 
• Artificial intelligence (AI) : According to a 2023 Invesco survey of systematic investors with $22.5 trillion under management, quants are not using AI extensively right now because of the technology's current challenges—the complexity and interpretability of the models and the quality of available data. But familiarity with AI and its uses will be important going forward.

The average base salary for quant traders, according to recent statistics from Indeed.com.

Beyond the above-mentioned technical skills, quant traders also need soft skills. Those employed at investment banks or hedge funds may occasionally need to present their developed concepts to fund managers and higher-ups for approval. Quants do not typically interact with clients and they often work with a specialized team, so average communication skills may suffice. In addition, a quant trader should have the following soft skills:

• A trader's temperament :   Not everyone can think and act like a trader. Successful traders are always looking for innovative trading ideas and are able to adapt to changing market conditions, thrive under stress, and accept long working hours. Employers thoroughly assess candidates for these traits. Some even give psychometric tests.
• Risk-taking abilities :   The present-day trading world is not for the faint-hearted. Courtesy of margin and leveraged trading with dependency on computers, losses can reach amounts higher than a trader's available capital. Aspiring quants must understand risk management and risk mitigation techniques. A successful quant may make 10 trades, face losses on the first eight, and profit only with the last two trades.
• Comfortable with failure : A quant keeps looking for innovative trading ideas. Even if an idea seems foolproof, dynamic market conditions may render it a bust. Many aspiring quant traders fail because they get stuck on an idea and keep trying to make it work despite hostile market conditions. They may find it difficult to accept failure and thus be unwilling to let go of their concept. On the other hand, successful quants follow a dynamic detachment approach and quickly move on to other models and concepts as soon as they find challenges in existing ones.
• Innovative mindset :   The trading world is highly dynamic, and no concept can make money for long. With algorithms pitted against algorithms and each trying to outperform the others, only the one with better and unique strategies can survive. A quant needs to keep looking for new innovative trading ideas to seize profitable opportunities that may vanish quickly. It is a never-ending cycle.

Not all employers have hard and fast rules on academic credentials, but they will likely be looking for relevant experience and skills that are transferable. Getting a finance internship with an investment bank, for example, after college may help you build necessary skills for the job.

Getting an entry-level position as a research analyst for a hedge fund or other financial institution, for example, can allow you to acquire skills using machine learning software and large data sets It will also afford you the chance to gain industry knowledge and connect with professionals in the field who can help you move to the next level.

What Does a Quant Trader Do?

Quantitative traders, or quants, work with large data sets and mathematical models to evaluate financial products or markets in order to discover trading opportunities.

How Much Do Quants Make?

According to ZipRecruiter, annual salaries can range from a low of $98,000 to a high of $259500. Average pay is $169,729 a year, but it varies considerably, which ZipRecruiter notes "suggests there may be many opportunities for advancement and increased pay based on skill level, location, and years of experience."

What Degrees Do Quants Get?

Typically, to be a quantitative trader you need at least a bachelor's degree in a field like mathematics, statistics, finance, or computer science. Employers often prefer candidates who have a graduate degree, such as a master's in mathematical finance or even a PhD in a quantitative field like mathematics, statistics, physics, or computer science.

Quant trading requires advanced-level skills in finance, mathematics, and computer programming. Big salaries and sky-rocketing bonuses attract many candidates, so getting that first job can be a challenge. Beyond that, continued success requires constant innovation, comfort with risk, and long working hours.

Investment News. " What Will AI Do to Quants? Here's What $23T Investors Think ."

Indeed. " Quantitative Trader Salary in United States ."

ZipRecruiter. " Quantitative Trading Salary ."

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How to Become a Quantitative Analyst

Quantitative analysts or quants, as they are usually called, formulate and implement complex algorithms, statistical methods, and mathematical models to analyze data concerned with risk assessment, portfolio management, and market pricing.

These professionals aim to translate complex data into valuable insights so that finance and investment banks can make business decisions that take advantage of favorable market conditions and minimize risk. Read the following article to find out everything about how to become a quantitative analyst.

What Is a Quantitative Analyst?

A quantitative analyst uses mathematical and statistical models in research or study to assess the value of assets, determine the level of risk, develop trading strategies, and ascertain the market share of a company.

These experts use quantitative techniques such as linear programming, regression analysis, game theory, simulation, data mining, and probability to conduct research. This research could be descriptive, comparative, experimental, or correlative.

Quantitative Analyst Job Description

Quantitative analysts conduct market research and use descriptive and inferential statistics to solve complex financial problems. They evaluate and assess the performance of companies using environmental analysis and financial models.

These experts work with financial analysts to evaluate securities trading systems, market trends, and conditions by designing adequate variables and metrics. They then interpret the results of their financial data analysis and provide comprehensive reports of their findings to aid decision-making. They also validate data processing methods.

Quantitative Analyst Salary and Job Outlook

The average salary for quantitative analysts in 2021, according to ZipRecruiter, is $118,652. This figure is higher than in previous years due to a rise in the demand for these professionals. The US Bureau of Labor Statistics projects that the number of job openings for these professionals will grow at a six percent rate, faster than the average career. Between 2020 and 2030, we should expect 41,000 new jobs in the industry every year .

Top Reasons to Become a Quantitative Analyst in 2021

There are many reasons why landing a job in quantitative analytics could set you on the path toward a very successful career. Below is a list of reasons why this career choice is such a strong one.

• It is an esteemed job role in the trading world. Quantitative analysts are seen as the gurus of the financial industry.
• There is a projected high demand for this job role. Because of the stock exchange and trading market volatility, there will always be a need for analysts to nudge investors in the right direction.
• It has an attractive starting salary. Imagine working in a role that can make you a millionaire in less than a year. That's how lucrative this field can be.
• It is the future of the financial stock market.  This field is the future of business. The information derived from quantitative analyses has become a necessary prerequisite for strategy development.

Quantitative Analyst Job Requirements

To qualify for a quantitative analyst position, you will need to prove to the employer that you have the necessary education. Most quantitative analysts choose to get a bachelor’s degree and many go on to pursue graduate programs. In addition, they need a variety of technical skills. Below we take a closer look at the main skills they are expected to have.

• Expertise in quantitative finance. Knowledge of mathematical finance is essential, as is being able to perform complex statistical and mathematical calculations and process large volumes of data.
• Skill in computer programming. These professionals must have programming skills and the ability to write code and scripts for designing analytical models and software.
• Proficiency in productivity software. The Google and Microsoft suites, as well as similar productivity tools, are important when it comes to preparing reports and communicating findings.
• Research skills. Quantitative analysts must be able to employ appropriate research methods and tools to gather valuable insights into the financial market and industry.

Types of Quantitative Analyst Careers

If you want to build a career as a quantitative analyst or quant, there are a couple of available job options for you. Below we describe a few alternative career pathways a quantitative analyst could take.

Quantitative Risk Analyst

Risk analysts determine the level of risk involved in an investment or asset class by ascertaining and comparing records of economic conditions, company performance, and stock market variations.

Quantitative Researcher

Quantitative researchers employ different research methods using both structured and unstructured data sources. The goal is to provide valuable insights to users of financial information.

Model Validation Analyst

Model validation analysts make modeling decisions by assessing financial quantitative models, statistical tools, analytics programs, and software employed by financial institutions and commercial banks. These experts review the quantitative rules and statistical models that help financial institutions manage credit and financial risk levels.

Quantitative Analyst Meaning: What Does a Quantitative Analyst Do?

Quantitative analysts perform an array of tasks that are as complex as they are vital to a business’s finances. Below, we have compiled a list of the most important functions performed by these experts.

Risk Analysis and Management

This involves simultaneously limiting the risk of failure of a project while increasing its chances of success. Risk management is the act of identifying and mitigating possible threats to the success or completion of a business project.

Model Design and Validation

This is the process of developing and testing the efficiency of financial modeling tools and devices used in analysis. The goal is to ensure that these models comply with recommended standards and that they meet their intended business use.

After they conduct their research, quantitative analysts help businesses make decisions that ensure project success. These professionals use their findings to advise management and executives on the best way forward.

Essential Quantitative Analyst Skills

Quantitative analysis requires a wide range of hard and soft skills. Whether they are working for hedge funds or investment firms, these professionals must have technical expertise in various facets of the financial business process. Most importantly, they must demonstrate exceptionally advanced knowledge in the following areas.

Programming Skills

Quantitative analysts should be able to write code that enables them to process and store data. These experts should also be able to develop analytical software capable of conducting complex data analysis.

Communication Skills

Quantitative analysts need to be able to communicate their findings effectively to business executives so that they can make informed decisions. This requires good writing abilities to produce easy-to-understand reports as well as interpersonal skills to deliver effective presentations and advice.

Analytical and Problem-solving Skills

Quantitative analysts are problem solvers. They explore datasets using their math, statistical, and analysis skills to find patterns and solutions to pressing business questions. Without the ability to analyze a situation and think critically, they wouldn’t be able to provide sound financial advice.

How Long Does It Take to Become a Quantitative Analyst?

Most quantitative analysts complete a four-year undergraduate degree in statistics, mathematics, computer engineering, finance, or business. Depending on your career goals, you may also want to pursue an advanced degree or professional certificate in quantitative analysis.

A master's degree generally takes two years, while a PhD in Advanced Quantitative Methods normally takes four to seven years.

Can a Coding Bootcamp Help Me Become a Quantitative Analyst?

Yes, it can. An essential part of quantitative analysis is data analysis and programming, and these skills can be learned in a coding bootcamp. These programs could provide you with a broad knowledge in data analysis, software engineering, and system programming. This knowledge will help you develop an innovative mindset that could be beneficial in your career.

Can I Become a Quantitative Analyst from Home?

Yes, you can. Like with many other professions, most of the skills you need to become a quantitative analyst can be acquired remotely via an online coding bootcamp or even by enrolling in a university degree online. However, keep in mind that your presence may be required for certain discussions and work projects.

In addition, there are many online courses to learn quantitative methods . You can find high-quality courses on platforms like Khan Academy and edX to learn the ins and out of the different quantitative methods and types of research.

How to Become a Quantitative Analyst: A Step-by-Step Guide

If you’re thinking of starting a career in quantitative analytics, the following step-by-step guide will be useful in helping you get started. While there are many paths to a career in this field, the following is probably one of the most common ones.

Step 1: Get a College Degree in a Related Field

Foundational knowledge in this field is extremely important. You can’t become a quantitative analyst unless you have strong background knowledge. Even for an entry-level job role, you need an education in statistics, mathematics, computer engineering, finance, business, or a related discipline.

Step 2: Gain Work Experience

If you’re unsure what specialization to pursue in this industry, it’s a good idea to first accrue some actual experience. Increasing your practical knowledge and hands-on experience through a quant internship or an entry-level role will go a long way in narrowing down your career path.

Step 3: Specialize

There are several career paths in this field. Some quants specialize in algorithmic trading or statistical arbitrage while others focus on derivative pricing or quantitative investment. Once you choose a specialization, it is time to acquire further education in your chosen field to have access to new career opportunities. This includes master’s degrees, doctorates, and professional certification.

Best Schools and Education for a Quantitative Analyst Career

Although most professionals in the field of quantitative analysis choose the traditional route of studying at a university or college, there are alternative paths that are also worth considering. In most cases, some of these alternatives work well to complement university education.

Below we look at the educational options for this field in more detail.

Quantitative Analyst Bootcamps

There aren’t many bootcamps dedicated purely to quantitative analysis. Fortunately, there are many bootcamps in data science and data analysis. These programs equip students with data analytics skills that are essential for a career in quantitative analytics. The best data analytics bootcamps will enhance your skills and open up the doors to exciting new opportunities.

Vocational School

It might be hard to find a suitable program at a vocational school if your sights are on a career in quantitative analysis. Vocational schools generally focus on practical skills for trade occupations, which doesn’t really apply to this field. We recommend that you look for other options, such as coding bootcamps or university degrees.

Community College

Most community colleges don’t offer degrees in quantitative analysis. However, you can consider a finance or computer engineering-related degree. There are many community colleges around the country with highly regarded degrees in this fields, including Mesa Community College in Arizona and Jefferson State Community College in Alabama.

Quantitative Analyst Degrees

Securing a bachelor’s degree is important in this field. If you want access to more senior roles and higher pay, consider also a master’s degree. Some of the best schools in the nation to pursue such degrees are the Massachusett Institute of Technology, American University, Vanderbilt University, University of Texas, and the University of Southern California.

The best doctorate programs in quantitative analysis can be found at Purdue University and the University of Chicago. With a PhD in the field not only will you have your pick of jobs in the industry, but you will also be able to teach at the university level.

The Most Important Quantitative Analyst Certifications

Going a step further and acquiring an industry-approved professional certification will position you as an authority in your field. You will also be able to apply for higher-level jobs and bring home a bigger paycheck. The following are some certificates you should consider.

Certificate in Quantitative Finance

This certification in quantitative finance is a three-part program that begins with lessons on maths, finance, and programming to prime students. Next, students engage in a series of lessons, projects, and exams on quantitative finance and work toward their certification. Even after the course, they have access to a vast library of lectures to continue their journey into quantitative finance.

Advanced Risk and Portfolio Management Certificate

The Advanced Risk and Portfolio Management (ARPM) Certificate consists of three exams. These exams will test your broad knowledge in advanced data science, quantitative risk analysis, portfolio construction, and asset management.

Financial Modeling & Valuation Analyst Certification

The Financial Modeling & Valuation Analyst (FMVA) Certification covers topics such as financial modeling, valuation, financial analysis, and quantitative techniques. It’s open to financial analysts professionals and quantitative analysis graduates who want to upscale in their careers by learning about the financial industry.

How to Prepare for Your Quantitative Analyst Job Interview

Quantitative analyst interviews will focus both on the personality and technical abilities of the candidate. They are the perfect opportunity to show potential employers that you are qualified and have the skills necessary to exceed in the role. We have compiled a list of technical questions to help you prepare for a potential interview .

Quantitative Analyst Job Interview Practice Questions

• If you were given 52 decks of cards, what is the probability of drawing a queen in your first pick?
• What is an ensemble model, and what are the examples of algorithms used in the ensemble method?
• Tell me about a project you completed. Which regression model did you use to arrive at an answer? What assumption parameters were used?
• Suppose you have a list of numbers starting with 10 all the way through 69. What is the aggregate value of the combined numbers?

Should I Become a Quantitative Analyst in 2021?

Yes, absolutely. If you enjoy solving complex mathematical equations and analyzing large volumes of financial data, then this career would be perfect for you. Because these experts are in such high demand, you will earn a high salary and have little trouble landing your dream job once you are qualified.

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Data Science

How to Become a Quantitative Analyst [+ Salary & Career Guide]

What is a Quantitative Analyst?

What does a quantitative analyst do (+ average day).

• Career Outlook
• Companies Hiring
• Paths to Careers
• Education Requirements

Quantitative analysts (often called “quants” for short) are described by Investopedia as “ the rocket scientists of Wall Street .” Currently in high demand thanks to their advanced skills in mathematics, finance, and technology, quantitative analysts typically command high salaries. How high?

Read on to learn more about:

• Quantitative analyst salary data
• Quantitative analyst job growth and career outlook
• Companies across industries hiring quantitative analysts
• Quantitative analyst hard skills and soft skills
• How to become a quant
• Quant career paths and educational requirements
• Answers to frequently asked questions

Quantitative analysts are professionals who specialize in “the design, development, and implementation of algorithms and mathematical or statistical models intended to solve complex financial problems,” according to the Corporate Finance Institute . “In their work, quantitative analysts apply a blend of techniques and knowledge from multiple disciplines including finance, economics, mathematics, statistics, and computer science.”

Investopedia defines quantitative analyst as: “A professional who uses quantitative methods to help companies make business and financial decisions. Investment banks, asset managers, hedge funds, private equity firms and insurance companies all employ quantitative analysts to help them identify profitable investment opportunities and manage risk.”

Quants are in particularly high demand in the world of investing and securities trading because of their ability to develop valuable insights intended to give their employers a competitive edge. “While the computer algorithm does the grunt work, it is quantitative analysts who are the brains behind these algorithms,” says Investopedia. “The best in the business make their employers millions of dollars on a monthly basis simply by programming algorithms that are fast and efficient enough to locate the best trades before the competition.”

For those with the right skills, education and experience, “a position as a quantitative analyst is financially lucrative and intellectually stimulating,” according to QuantStart . However, “the competition for roles is tough, especially within top tier funds and investment banks.”

Commonly employed by a broad spectrum of financial industry organizations (securities firms, commercial banks, investment banks, wealth management firms, hedge funds, etc.), quants are also employed by insurance companies, accounting firms, management consulting organizations, and financial software companies, among others.

Often their core responsibilities revolve around using advanced quantitative methods to scope out opportunities and evaluate risk. The work of a quantitative analyst is nearly always connected to quantitative research; according to Street of Walls , financial industry quants are typically focused on:

• Improving the trading architecture used to place trades
• Improving the signals used to evaluate trade ideas
• Reducing transaction costs and/or market impact
• Managing portfolio risk
• Testing and deploying new trading strategies

Street of Walls notes that daily activities may include:

• Reading academic literature to help ground trading strategies in theory
• Creating and testing a hypothesis for a given trading strategy
• Back-testing the strategy with existing data on an out-of-sample basis using actual transaction costs
• Programming and implementing the trading strategy
• Stress-testing the strategy to gauge environments in which the strategy may fail to perform
• Creating a risk-management framework for implementing the strategy and proposing a systematic method for increasing or decreasing portfolio risk

The work is often considered challenging to explain in layman’s terms. In this Investopedia video , three quantitative analysis professionals talk about the nature of their work, including one who needed to figure out a way to “explain what I do to my mother.”

Quantitative Analyst Career Outlook [Demand is High]

In the trading world, quantitative analysts are especially in demand. “As financial securities become increasingly complex, demand has grown steadily for professionals who not only understand the complex mathematical models that price these securities, but who are able to enhance them to generate profits and reduce risk,” reports Investopedia . Demand for talent driven by these and other factors:

• The rapid growth of hedge funds and automated trading systems
• The increasing complexity of both liquid and illiquid securities
• The need to give traders, accountants, and sales reps access to pricing and risk models
• The ongoing search for market-neutral investment strategies.

How Much Does a Quantitative Analyst Make?

Six-figure salaries and above are the norm for quantitative analysts. Salary figures tend to vary widely (and estimates from employment websites are often updated in real time), but reports of $100,000+ salaries are consistent across the board for quantitative analysts.

The U.S. Bureau of Labor Statistics reports the following average and upper-level salaries for financial and investment analysts, by sector:

• Securities, commodities, etc. – $125,040
• Investment pools and funds – $132,350
• Software publishing – $110,930
• Other information services – $110,150

Here are some additional examples:

• $135,846 ( Indeed )
• $117,000 ( Talent )
• $112,000 up to $174,000 ( Glassdoor )
• $106,751 ( Investopedia )

Compensation for highly skilled quants at hedge funds and other organizations may also include large bonuses, according to Street of Walls , which quotes a survey showing an average total compensation of $230,000.

Companies (By Industry) Hiring Quantitative Analysts

Anyone who is curious about how to become a quant will also be interested in taking a closer look at the vast array of big-name and lesser-known companies who are looking to hire quantitative analysts.

As mentioned above, a lot of the action is in the financial industry, but there are also career opportunities for people skilled at quantitative analysis in insurance, accounting, software, consulting, social media, and more. During a recent LinkedIn search, the NFL’s Philadelphia Eagles were seeking a “Quantitative Analyst – Football Operations.”

A recent LinkedIn search provides a look at some of the best-known organizations seeking quantitative analysts, including:

• Fidelity Investments
• Bank of America
• Goldman Sachs
• Magnetar Capital
• Penn Mutual
• Navy Federal Credit Union
• The Motley Fool
• Capital One
• And countless more

Quantitative Analyst Hard Skills

“In general, quantitative analysts use their mathematical and statistical skills to detect market changes,” says Study.com . “Advanced knowledge of calculus, engineering and game theory is key; programming skills are essential, particularly in C++. A strong background in modeling with large amounts of data is also mandatory.”

In terms of the key skills, knowledge, and aptitude needed to excel as a quantitative analyst, the bar is pretty high. Not all of these skills are needed for every job, but the following are some of the key skills and subject areas that help position quants for career success:

Investopedia emphasizes advanced understanding of mathematical computation, including:

• Calculus (including differential, integral, and stochastic)
• Linear algebra and differential equations
• Probability and statistics

Other sources make reference to:

• Strong abilities in data mining and data analysis
• Extensive financial knowledge
• Programming skills
• And, of course, software skills

Advanced software skills are “critical to job performance,” reports Investopedia. “C++ is typically used for high-frequency trading applications, and offline statistical analysis would be performed in MATLAB, SAS, S-PLUS or a similar package. Pricing knowledge may also be embedded in trading tools created with Java, .NET or VBA, and are often integrated with Excel. Monte Carlo techniques are essential.”

Quantitative Analyst Soft Skills

As in many higher-level analyst and data science-related jobs, well-rounded soft skills are also essential, with many job listings emphasizing excellent written and verbal communication skills. This is because, in addition to generating high-value work product, analysts must be skilled at documenting and presenting their findings to others within their organization.

Other general, less technically specific skills include:

• Teamwork and collaboration
• Problem solving
• Proficiency at research
• Leadership capabilities

Paths to Become a Quantitative Analyst [Degree vs. Non-Degree]

There is some non-degree-oriented training you can do to enhance your chances for career success, but most quantitative analyst jobs require an advanced degree and in some cases a Ph.D. Examples of industry certifications pursued by some professionals in this field include a Chartered Financial Analyst (CFA) designation or a Certificate in Quantitative Finance (CQF).

Education Requirements for a Quantitative Analyst

When hiring quants, “most firms look for at least a master’s degree or preferably a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics. Master’s degrees in financial engineering or computational finance are also effective entry points for quant careers,” says Investopedia. Other master’s degrees that connect to the field of quantitative analysis include:

• Data science
• Mathematical finance
• Financial engineering

Because of the strong emphasis on working with data and using advanced skills to uncover actionable insights, an advanced degree in data science can be an extremely helpful stepping stone.

The Corporate Finance Institute reports, “The rapid introduction of data science and machine learning in finance [has] increased the demand for candidates with the relevant educational background in said fields.” And Wall Street Mojo cites data science as one of four key areas of expertise, saying, “To be able to thrive in this career, you need to be amazing at four specific subjects – mathematics, data science or software, finance, and application development.”

Quantitative Analyst vs. Other Similar Job Titles

Here are some additional job titles in quantitative analysis and related fields:  

• Quantitative Trader
• Quantitative Researcher
• Quantitative Developer
• Financial Engineer
• Investment Analyst
• Risk Analyst
• Quantitative Portfolio Manager
• Data Analyst (Finance)
• Quantitative Software Engineer
• Quantitative Investment Manager
• Manager, Data Science and Analytics
• Data Scientist, Consumer Analytics

Quantitative Analyst vs. Data Scientist [Similarities and Differences]

“Quants and data scientists have more in common than you might think. The differences between them are shrinking as tech plays a more prominent role in finance.”

That’s the intro to a report on the similarities and differences between these two roles that rely on advanced analytical skills to derive valuable business insights from complex sets of data.

One of the biggest differences is that skilled data scientists have perhaps a broader horizon of potential career pathways, since quants are employed largely by sectors dealing in high finance and risk analysis.

The report mentioned above also cites noted finance columnist Matt Levine arguing that “there is no hard-and-fast dividing line between quants and data scientists, and that eventually, the baseline expectation for quantitative analysts will be fundamentally the same as those for data scientists.”

In a 2018 Bloomberg column titled “Quants Might Not Be So Special Someday: In the Future, Everyone Will Be a Data Scientist,” Levine envisions a future in which “every reasonably sophisticated investment firm will have a data science department.” Learn more about the Data Scientist vs. Quantitative Analyst debate in these articles by Springboard and Noodle.com .

A data science master’s degree, such as the online Master of Science in Applied Data Science , will typically offer a solid foundation in technical and soft skills that are also extremely valuable to aspiring quants, including the widely used open-source programming languages Python and R.

FAQs About Quantitative Analyst Careers

Q: What is the average salary for a quantitative analyst?

A: Salaries for quantitative analysts often range well above $100,000. Employment website Indeed cites an average salary for a quantitative analyst in the U.S. of $134,000.

Q: What are the key differences between quantitative analysis and data science?

A: One of the biggest differences is that quantitative analysts are highly sought after in the finance sector, while data scientists are in demand across a far broader range of industries, including financial services, technology, cybersecurity, media, health care, retail, manufacturing, and more.

Q: Would a master’s in data science help me pursue a career as a quant?

A: Yes. A strong data science master’s degree engages graduate students in many of the same technical and soft skills that are also extremely valuable to aspiring quantitative analysts.

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How to Become a Quant Scientist (And make $259,384 per year!)

A 'Quant Scientist' is a specialized role at the convergence of Math & Statistics, Python programming, and Market Intuition, boasting an earnings potential of up to $259,384 — double that of a Quant Analyst. While Data Scientists and Quant Developers tap into two of these disciplines, the comprehensive skill set of a Quant Scientist makes them a coveted asset in the trading world. We'll cover:

What is a Quant Scientist (and why is this NEW role is in high demand)?

The 3 Core Skills of a Quant Scientist

The 4 Quant Scientist Venn Diagram Intersections

How to become a Quant Scientist (and make $259,384 per year !) 👇

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Want exclusive access to our FULL codebase for this Quant Science tutorial plus dozens more? Then:

Free Video Tutorial: How to become a Quant Scientist

This post comes with an 5 minute video tutorial sharing even more about the Quant Scientist role than we cover in this overview.

What is a Quant Scientist?

Recently, a picture that Matt posted sent the internet into a frenzy, prompting this deeper dive into what a 'Quant Scientist' is, the skills required, and its significance in today's job market.

Why is the demand for the Quant Scientist role is rising?

In the rapidly evolving landscape of algorithmic trading and financial analysis, the demand for Quant Scientists is soaring (-- See the $259,000+ salary), and here's why:

1. The Speed & Complexity of Modern Financial Markets:

As financial markets become more sophisticated, there's an escalating need for professionals who can navigate these complexities and perform research fast. Quant Scientists, with their combined expertise in Math & Statistics, Python, and Market Intuition, can decode intricate market patterns, understand multifaceted financial instruments, connect to a variety of data sources and trading APIs, and devise strategies that both anticipate and react to market actions in real time .

2. Reducing Risk & Maximizing Profit:

The holistic knowledge of a Quant Scientist allows for the development of more robust, resilient trading strategies. Their deep understanding of the markets , combined with the mathematical models they craft, means they can identify opportunities and risks that others might miss, translating into potentially higher profits and minimized losses. And with the explosion of big data, firms now have access to an unprecedented volume of information. Quant Scientists can harness this data, leveraging their Python skills to apply advanced analytics and machine learning, translating raw data into actionable insights. This is invaluable for companies seeking a competitive edge in today's data-driven world.

3. A Multi-disciplinary Approach:

In an industry where specialization was once the key, the tides are turning towards professionals who can wear multiple hats. Firms are on the lookout for individuals who aren't just confined to one niche but can interlink various domains to generate innovative solutions. The Quant Scientist, being the embodiment of this multi-disciplinary approach, becomes an indispensable asset.

As you can see, the Quant Scientist role isn't just a buzzword . Their unique blend of skills and the rising intricacies of the financial world put them in a prime position, making them one of the most sought-after professionals in the financial industry today.

The Problem with Existing Roles:

Data Scientist : This role emerges at the intersection of Math & Statistics and Python. With a background in math and stats, combined with proficiency in Python, individuals typically fall into the data scientist category. The average salary for someone with 3-5 years of experience in this role ranges from $124,000, according to platforms like indeed.com . These roles lack trading experience and market intuition.

Quant Analyst & Quant Developer : Merging Math/Statistics with Market Intuition results in roles like Quant Analyst or Quant Developer. These professionals have a deep understanding of the markets, perhaps even having traded personally. They harness math (like linear algebra and stochastic calculus) and basic statistics (like linear regression) to devise models around financial instruments like options and interest rate derivatives. Such roles can fetch around $138,000 annually. However, people in these roles stagnate because they lack the Python coding expertise to do algorithmic trading, backtesting, and implementation.

The Danger Zone (Python + Markets) : Treading into the intersection of Python and Market Intuition without a solid foundation in math and statistics can be risky, hence the symbolic 'skull and crossbones'. Those who directly dive into market strategies with coding, without understanding the underlying assumptions, are bound to face losses. A common misconception, for instance, is that market returns follow a normal distribution. Depending solely on such a baseless assumption can result in severe financial setbacks.

What skills makes a Quant Scientist?

The Quant Scientist is the convergence of 3 Core Skills:

Math and Statistics

Python (Programming)

Market Intuition

1. Math and Statistics:

Model Building: At the heart of quantitative analysis is the creation of models to predict market behaviors. These models require rigorous mathematical foundations, ensuring they are both accurate and reliable.

Analytical Rigor: The financial markets are complex, and understanding them demands a structured approach. Statistical methods allow Quant Scientists to discern patterns, test hypotheses, and validate their theories about market behaviors.

Risk Management: Properly understanding and quantifying risk is crucial in trading. Advanced statistical tools help in assessing potential risks, ensuring that investment decisions are informed and deliberate.

2. Python (Programming):

Data Handling : In the age of big data, Python stands out as a versatile tool for data extraction, manipulation, and visualization. It's crucial for handling vast datasets that are now commonplace in financial analysis.

Algorithm Development : Trading strategies are implemented as algorithms that automatically make buy or sell decisions based on data. Python is a leading language in this realm due to its simplicity and the vast libraries available for scientific computing.

Rapid Prototyping : In the fast-paced world of finance, strategies need to be tested quickly. Python allows for rapid prototyping of ideas, enabling Quant Scientists to validate or discard hypotheses efficiently.

3. Market Intuition:

Real-World Context : While models and algorithms are grounded in theory, they operate in the real world. Market intuition ensures that these tools are not just mathematically sound but also contextually relevant.

Anticipating Anomalies : Financial markets are affected by a myriad of factors – from geopolitical events to technological disruptions. A keen market intuition allows Quant Scientists to anticipate such anomalies and adjust their strategies accordingly.

Emotional Intelligence : Markets, driven by human behaviors, are not always rational. Market intuition brings an understanding of these emotional undercurrents, ensuring that strategies can navigate both the logical and the illogical aspects of trading.

The Skills and Tools in One Diagram

If you want to further dive into what comprises a Quant Scientist, check out the Quant Scientist Algorithmic Trading Framework .

Conclusion: The Quant Scientist is the Future.

The 'Quant Scientist' lies at the convergence of all three disciplines: math & stats, python, and market intuition. With a strong foundation in math and statistics, proficiency in Python, and robust market intuition, these individuals are the true unicorns of the trading world. Their comprehensive skill set, encompassing both academics and hands-on market experience, makes them the most sought after by hedge funds and recruiters alike.

Becoming a Quant Scientist:

Our course is crafted to cultivate the skillset of a Quant Scientist. It covers:

Essential math and statistics.

Python programming.

Paper trading, eventually leading to live trading, ensuring you gain that vital market intuition.

In summary, while the allure of the trading world is vast, it's crucial not to venture into 'the danger zone' without a solid foundation. Aim for the holistic skills of a Quant Scientist, and steer clear of pitfalls.

Until next time, avoid the 'skull and crossbones' and happy learning!

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• Education Advice

PhD vs Full Time Quant Researcher

• Thread starter alittlebear
• Start date 3/6/21

alittlebear

Hi all, I am a senior student who is about to graduate and now feel a little confused about the future. I want to be a top quantitative researcher and my goal is to enter a top hedge fund. I have two options, one is to work after graduating from a master's degree, and the other is to continue to pursue a PhD degree. I have received some offers suitable for pursuing PhD in Statistics (Uchicago and Duke stat ms) and some offers suitable for pursuing PhD in Finance (Columbia Business School FinEcon ms). If choose to work after graduation, I may need to constantly change jobs to achieve my goals. My question is: 1. I saw many people continue to study PhD after reading MFE and finishing a decent summer intern, so I would like to ask whether the work of a full-time quantitative researcher is really as interesting as what people thought and do they have a steep learning curve? 2. Whether companys like DE Shaw and Citadel would prefer finance PhD or statistics PhD, or both have the opportunity to get into them directly? 3. What is the salary for PhD at the beginning and the salary for 5 years after MFE graduation? Assume that MFE students worked in big bank / top prop trading like IMC. 4. In the long run, is PhD more conducive to career development? I have passion in both finance and statistics so PhD degree is not a problem, and I also hope to learn more through PhD training.  

1. Yeah I’m a researcher at a buy side firm at the moment. Your learning curve is steep depending on what your experience is like. If you know your quantitative methods and techniques then you might have a sharp learning curve on the business side and vice versa. The job is suited for phds not because they have phds, but because they’re capable of learning new things quickly and well because no one holds your hand. If you can learn concepts quickly and maintain immaculate attention to detail, this is a good fit, because the work is great 2. Don’t do a finance PhD so you can get into a citadel. I have an undergrad and have gotten research offers at good prop trading firms and citadel. 3. don’t do a PhD expecting a massive salary boost unless you’re going to a old school firm. You’re going to end up having regrets because most of the top firms now will give an undergrad with 4-6 years work experience in quant research more or the same as a PhD just starting their career (unless you have some truly exceptional research relevant to the firm or some prior work experience) Also IMCs comp isn’t that great, they lock a lot of it away behind deferred comp to force you to stay at the firm 4. I’m the son of two phds and work with phds daily so I can take some sort of stab at this, but I would say yeah depending on what your goal is. If your goal is to just be a quant, then no. If your goal is to maybe be a professor, or a researcher at a PhD only lab, or consult a central bank, or even start your own firm/ strategy then yeah, it will give you the skills and qualifications necessary however given your stated career goals I don’t think a PhD is a good fit. If you’re passionate about learning about finance / stats, you can do it yourself too without taking the 4-6 year hit on your career. Top finance firms like mine or citadel don’t really care how much finance knowledge you’re bringing out of your undergrad because they feel like they can train you. If you don’t think you can get a job in the industry without a masters, then go for the subject that you’re more interested in and get the masters. Definitely don’t pursue a PhD if your end goal is just to be a industry quant unless you’re purely looking to learn  

Kinda unrelated but sorta related to the spirit of your post, but IMO the things that make a good researcher are 1. An ability to pick up new concepts quickly 2. The ability to implement their ideas into production code 3. Having a solid understanding of financial markets. No one is expecting you to know what makes a good trade (unless you’ve got prior trading experience) because a MFE or a PhD aren’t really going to teach you that, but understanding the basic mechanics of the market you’ve chosen to work in (I.e if in credit trading you should know basic bond terminology and bond math, common trading techniques in bonds is a big plus). An MFE will help out with number 3, so if you feel like you have 1&2, post the MFE you’re probably good to go wherever. So you can see from those skills why a PhD does well in the job, but also why someone with just an undergrad or masters can as well. It’s usually the case that the undergrads that make it into these roles hit the three criteria out of passion/ coursework (similar to phds)  

tips said: 1. Yeah I’m a researcher at a buy side firm at the moment. Your learning curve is steep depending on what your experience is like. If you know your quantitative methods and techniques then you might have a sharp learning curve on the business side and vice versa. The job is suited for phds not because they have phds, but because they’re capable of learning new things quickly and well because no one holds your hand. If you can learn concepts quickly and maintain immaculate attention to detail, this is a good fit, because the work is great 2. Don’t do a finance PhD so you can get into a citadel. I have an undergrad and have gotten research offers at good prop trading firms and citadel. 3. don’t do a PhD expecting a massive salary boost unless you’re going to a old school firm. You’re going to end up having regrets because most of the top firms now will give an undergrad with 4-6 years work experience in quant research more or the same as a PhD just starting their career (unless you have some truly exceptional research relevant to the firm or some prior work experience) Also IMCs comp isn’t that great, they lock a lot of it away behind deferred comp to force you to stay at the firm 4. I’m the son of two phds and work with phds daily so I can take some sort of stab at this, but I would say yeah depending on what your goal is. If your goal is to just be a quant, then no. If your goal is to maybe be a professor, or a researcher at a PhD only lab, or consult a central bank, or even start your own firm/ strategy then yeah, it will give you the skills and qualifications necessary however given your stated career goals I don’t think a PhD is a good fit. If you’re passionate about learning about finance / stats, you can do it yourself too without taking the 4-6 year hit on your career. Top finance firms like mine or citadel don’t really care how much finance knowledge you’re bringing out of your undergrad because they feel like they can train you. If you don’t think you can get a job in the industry without a masters, then go for the subject that you’re more interested in and get the masters. Definitely don’t pursue a PhD if your end goal is just to be a industry quant unless you’re purely looking to learn Click to expand...

tips said: Kinda unrelated but sorta related to the spirit of your post, but IMO the things that make a good researcher are 1. An ability to pick up new concepts quickly 2. The ability to implement their ideas into production code 3. Having a solid understanding of financial markets. No one is expecting you to know what makes a good trade (unless you’ve got prior trading experience) because a MFE or a PhD aren’t really going to teach you that, but understanding the basic mechanics of the market you’ve chosen to work in (I.e if in credit trading you should know basic bond terminology and bond math, common trading techniques in bonds is a big plus). An MFE will help out with number 3, so if you feel like you have 1&2, post the MFE you’re probably good to go wherever. So you can see from those skills why a PhD does well in the job, but also why someone with just an undergrad or masters can as well. It’s usually the case that the undergrads that make it into these roles hit the three criteria out of passion/ coursework (similar to phds) Click to expand...

monbuchicassie

alittlebear said: Thanks for the reply! I think I do have the abilities but what I am facing is a more and more competitive job market. I am not from a top undergraduate school so it's pretty hard for me to find jobs in leading buysides after graduation from MFE. It would be pretty struggle for me if I start to work at sell sides. Also, I think most of the applicants are using interview book and leetcode to 'fit' the requirement of a job, instead of truly master the knowledge in statistics/math, which I think is quite boring. I am in awe of knowledge and also know my deficiencies in statistics, that's why I want to have a PhD degree Click to expand...

TheComplexUnit

(1) You'll learn more practical skills on the street than you would in any PhD programme. (2) Hedge funds are not 'maths departments'. In other words, you'd be surprised how easy the maths you'll need is. (3) Getting into so-called "top companies" is not necessarily what you want to aim for. Chances are you'll be pigeon-holed into a highly specialised semi-brain dead role. Aim for smaller companies with a start-up attitude. (4) These days, highly skilled developers with a modicum of quant skills are kings. Again: avoid the delusion that epsilon-delta will bring you glory.  

Since I posted last on this thread I've started becoming more involved with our hiring and here are the four things that make a perfect candidate for us 1. Great c++ / OOP/ software dev skills (can self teach, get from a degree, or from taking the quant net class, all of this + work experience / side projects usually leads to decent developers) 2. Quant skills (linear algebra, probability, common ML libraries, common optimization techniques like normal method, gradient descent, anything else is a plus) 3. Good research ability 4. attitude While no candidate is perfect and has all of those skills, we generally want to see candidates with at least 3 of these pillars before considering an offer. Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background. The "gap" in quant skills has definitely diminished as a plethora of strong ML courses / programs have become available to undergraduates, but there are very few quant research candidates with strong software engineering experience. Being a strong python programmer (note the use of programmer, not developer) while helpful for ad-hoc research, is usually not sufficient at a fully automated trading firm  

Daniel Duffy

C++ author, trainer.

What's "SWE background"? What distinguishes "programmer" from "developer"? It may be an idiosyncratic and have several definitions on who you ask. I would say most quants of any flavour should be able to test their ideas (in a computer).  

SWE = software engineering To me, a "programmer" is someone who has a basic to decent grasp of the syntax in some language (i.e knows the syntax for c++ / python ) enough to be self-sufficient at creating their own models and run backtests given enough time (I.e. a recent undergrad in a non-CS degree with some CS courses, someone who has solely taken a quant net class); A programmer might not understand the cost associated with copying vs pass by reference or the usefulness of move, but would understand enough to implement a basic linear regression given enough time A developer is someone who can take that model and architect the libraries / packages / whatever else is needed to productionize that model (keyword here is architect). Being a good architect might mean having a solid grasp of design patterns, a deep enough understanding of the language to understand trade offs of using certain data structures / keywords, the skill to write really good unit tests, and an ability to think about the future of any design choices made (i.e. if part of your model requires implementing something which should be generic to future models, are you implementing it in a generic way so another researcher can use it?) - This is usually someone who at bare minimum the credentials of a "programmer" but also has some work experience and / or a computer science degree from a decent program that teaches these skills. Finding a "strong developer" who has good quant skills is pretty hard IMO, and is part of what makes it so difficult to get a job as a quant researcher at one of the top prop shops / hedge funds that specialize in fully automated trading. Agreed fully that there are many definitions, and many different ways to satisfy the criteria I listed, just how we distinguish when hiring at my firm.  

By the way, what the difference betwen quant researcher and quant analyst? What kinds of skills do the two positions need?  

Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background Sounds logical. This happens when pure maths pde (theory only) have not learned to program. So, it would not be a surprise to me. It takes [7,20] years to really learn software design and apply it to anything. Software and programming is a skill to be learned. Many underestimate the task. The period 1970-1990 was the golden period of PDEs and applications to all kinds of stuff, just before the fall of the Wall.  

Daniel Duffy said: Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background Sounds logical. This happens when pure maths pde (theory only) have not learned to program. So, it would not be a surprise to me. It takes [7,20] years to really learn software design and apply it to anything. Software and programming is a skill to be learned. Many underestimate the task. The period 1970-1990 was the golden period of PDEs and applications to all kinds of stuff, just before the fall of the Wall. Click to expand...

tips said: Since I posted last on this thread I've started becoming more involved with our hiring and here are the four things that make a perfect candidate for us 1. Great c++ / OOP/ software dev skills (can self teach, get from a degree, or from taking the quant net class, all of this + work experience / side projects usually leads to decent developers) 2. Quant skills (linear algebra, probability, common ML libraries, common optimization techniques like normal method, gradient descent, anything else is a plus) 3. Good research ability 4. attitude While no candidate is perfect and has all of those skills, we generally want to see candidates with at least 3 of these pillars before considering an offer. Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background. The "gap" in quant skills has definitely diminished as a plethora of strong ML courses / programs have become available to undergraduates, but there are very few quant research candidates with strong software engineering experience. Being a strong python programmer (note the use of programmer, not developer) while helpful for ad-hoc research, is usually not sufficient at a fully automated trading firm Click to expand...

monbuchicassie said: By the way, what the difference betwen quant researcher and quant analyst? What kinds of skills do the two positions need? Click to expand...

tips said: To me, a quant analyst is really vague because its a term used throughout the job market, not just in finance. In most cases it's someone who is more of a "programmer" with basic to medium quantitative skills, basic to medium research ability; In very rare circumstances, they might do the same work as a researcher (like 15% of the time). Typically, these candidates have at least an undergrad, given that it is typically junior most of these candidates are fresh out of undergrad or a masters program A quant researcher is someone who is more of a "dev" with medium to strong quant skills, strong research ability (aka what most users here typically think of as a quant), though on occasion some jobs might actually be closer to a quant analyst (like 15% of the time, not an exact number this is just a guess based on my intuition); Typically, these candidates have graduate degrees (masters+ some work experience, PhDs, though strong undergraduate candidates can do this as well) Given the vagueness, you should always make sure to ask what the job/title means to whatever firm you're interviewing with since the definitions can be variable. The work of a researcher at an old school firm vs a fully automated firm is very different Click to expand...

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How to become a quant

• Quant finance jobs combine mathematical and engineering skills
• Quants in finance look for mathematical relationships between underlying assets, or create derivatives based on those assets
• Quants in finance also (increasingly) work in areas like risk
• You make the most money in quant finance when you’re closely associated with the profit and loss made by traders or portfolio managers.

What do quantitative finance jobs involve?

If you want to understand quantitative finance as a discipline, you should look at the winners of the Nobel Prize for Economics.

For much of the last century, financial decision making was based on heuristic principals, but in 1990 the prize went to Harry M. Markowitz, Merton H. Miller, and William F. Sharpe, in recognition of their mathematical approach to the study of financial markets and investment decision-making. In 1997, the award went to Robert C. Merton and Myron S. Scholes for their method for determining the value of stock options and other derivatives.

The 1990 award helped establish the so-called P-measure subfield, which was primarily concerned with the behaviour of the underlying assets – stocks, bonds, currencies, etc. The 1997 award formalized the creation of the Q-measure subfield, concerned with derivatives on those assets, such as options.

Quantitative finance (or quant finance) was born. It’s been evolving ever since.

Quantitative finance is a broad church. Before the financial crisis of 2007-2008, the most lucrative jobs in quantitative finance were found in the creation of the ever-more complex derivative products. Since the crisis, the emphasis has shifted to risk and complexity management, regulation, and robustness.

Today, quantitative finance is a catch-all term that covers numerous different subfields. If you have a quantitative finance job, you might be working in any of the following areas:

• Computational Finance : Computational methods, including Monte Carlo, PDE, lattice, and other numerical methods with applications to financial modelling.
• Economics : Including micro- and macroeconomics, international economics, theory of the firm, labour economics, and other economic topics outside finance.
• General Finance : The development of general quantitative methodologies with applications in finance.
• Mathematical Finance . Mathematical and analytical methods of finance, including stochastic, probabilistic, and functional analysis, algebraic, geometric, and other methods.
• Portfolio Management : Selecting and optimizing securities, capital allocation, investment strategies, and performance measurement.
• Pricing of Securities : The valuation and hedging of financial securities, their derivatives, and structured products.
• Risk Management : The measurement and management of financial risks in trading, banking, insurance, corporate and other applications.
• Statistical Finance : Statistical, econometric analysis with applications to financial markets and economic data.
• Trading and Market Microstructure : Looking at market microstructure, liquidity, exchange, and auction design, automated trading, agent-based modelling and market-making.

As a quant, these are some of the specific jobs you could do:

There are quant jobs creating derivative pricing models

Derivatives trading, especially exotic derivatives trading, exploded in the run up to the global financial crisis (GFC) and, after a few years of uncertainty that ensued, has started to grow again. According to the WFE Derivatives Report 2020 , over the last ten years, global derivatives trading volumes have increased by 40.4%, largely driven by an increase in equity derivatives trading in the last three years.

Whereas before the GFC the emphasis was on increasing complexity, e.g., the creation of exotic derivatives, after the GFC the focus has shifted to taming complexity and increasing the realism and robustness of pricing models. (See https://sites.google.com/site/roughvol/home/risks-1 for a list of articles on this subject).

The quants who work on derivatives pricing models are referred to as derivatives pricing quants or simply pricing quants . They may also be called Q-measure quants because they work under the risk neutral (Q) measure.

 There are quant jobs applying existing derivative pricing models

Not all Q-measure quants have the opportunity to contribute new derivatives pricing models. Risk aversion also dictates that instead of developing something new, one should go for the tried and tested solutions. Therefore, most quants simply implement and customize models that have been created by someone else.

This doesn’t mean there’s no room for innovation. – You can engineer custom solutions around existing models. This is why the term financial engineering is often used in preference to quantitative finance to describe this kind of work. Financial services firms are prepared to pay handsomely for both these activities.

There are quant jobs creating new products

Financial engineering and, more broadly , financial innovation often take the form of the creation of new financial products. Even though there is a large array of classical exotics (digital options, barrier options, look-back options, Asian options, options on baskets, forward-start options, compound options, etc.)…, there is still scope for new ideas and occasionally we see some radically new and useful products.

Nowadays though, instead of creating new exotic products, financial services firms often manufacture the so-called structured products . These are pre-packaged financial products for facilitating customized risk-return objectives based on the returns from certain investible assets. Structured products can offer the exposure for specific market views and desired risk profiles under the constraints of financial budgets and legal frameworks for investment.

The experts that work on structured products are usually referred to as structurers rather than quants, although the work of a quant and that of a structurer has a significant overlap.

There are quant jobs creating trading strategies

Whereas the pricing of derivatives usually takes places under the risk-neutral (Q) measure, the design and development of trading strategies is a P-measure activity. This is why those who engage in it are usually called P-measure quants. Their skillset is often different from that of derivatives pricers: derivatives pricing relies on applied mathematics, such as the solution of partial differential equations and stochastic analysis, whereas P-measure work relies on different kinds of mathematics – such as those described in the book The Elements of Statistical Learning (statistics and, increasingly, machine learning).

On the surface, statistics appears easier than applied mathematics. It doesn’t involve such deeply nested formalisms (e.g. one doesn’t rely as much on measure theory in statistical work). However, the successful application of statistical methods to derive trading strategies with high Sharpe ratios is a highly challenging endeavour.

P-measure quants vary dramatically in outlook and skillsets. There are a few successful quants that have developed (or adopted) one or two profitable trading strategies and have built their careers around them. However, this is rare, since individual strategies are subject to alpha decay and what works today may fail to work tomorrow. Therefore, many quants invest their time and efforts in the development of sufficiently general methodologies and frameworks (be it scientific or software) that enable them to quickly generate new trading strategies and adapt the existing ones. Many trading firms have taken this activity to an industrial level; they constitute “factories” for the mass production of trading strategies. Others provide services to these trading firms, e.g. in the form of software, connectivity, data, etc.

Much of the time of a P-measure quant is spent on backtesting trading strategies and ideas (testing predictive models on historical data).

When you’re creating trading strategies, the nature of your job as a quant varies dramatically by trading frequency / holding period and asset class. Quants working for high-frequency trading firms, for example, build their strategies on tick data which arrives every millisecond, microsecond, or nanosecond, whereas quants working for longer-term asset managers (more on them later) look at hourly or daily returns.

There are quant jobs validating existing pricing models and trading strategies

Since the financial crisis, pricing models and strategies have been subjected to increasing scrutiny. Trading disasters, such as the 2012 Knight Capital stock trading disruption and the flash crashes, which happen every couple of years in different asset classes, have also contributed to the regulatory attention. Regulatory frameworks, such as MiFID II in Europe, require that the nature of the trading strategies be disclosed to the regulators and stipulate requirements for an audit trail.

Regulatory attention alone is not the only reason why pricing models and trading strategies should be carefully validated. Trading firms themselves are naturally interested in their validation. Trading strategies and, especially, derivatives pricing models are often very complex and nontrivial. Experts other than their creators (and not subject to the same conflicts of interests) are therefore requested to validate them.

This need has given rise to a different quant speciality – model validation quants.

On the one hand model validation quant jobs are less “glamorous” than that of the originators of new models and strategies. They suit the more detail-oriented people who don’t like to work under the pressures of the front office. Model validators work to less stringent deadlines and they have the opportunity to thoroughly test the ideas of others (and learn from them). As a by-product of their activities, they are often responsible for writing the documentation.

There are quant jobs on the trading floor

The closer you are to the profit and loss (pnl) made from trading, the more money you’ll typically be paid as a quant. Most quants don’t own the pnl. Instead, the trading (short-term) and investment (long-term) decisions are made by others – traders and asset managers.

However, the boundary between the two roles can be quite blurry. For example, in algorithmic trading businesses the quants are responsible for developing the trading strategies. The role of a trader – in this context called the book runner – is more formal and less creative than that of a quant. Since the trading decisions have already been made by the quant’s software, the book runner’s role amounts to vetting or validating these decisions after the fact. In practice the quant and the book runner must work closely together for the trading endeavour to be successful.

By comparison if you’re a quant pricing derivatives and writing derivatives pricing software, you’ll often lack hands-on trading and hedging expertise, and you won’t have client relationships. You’ll know in more detail than the trader how the products are priced, but it’s the trader who owns the dynamic hedging know-how – and it’s the trader who is usually compensated for it.

Many options traders themselves come from quantitative backgrounds and have previously worked as pricing or desk quants (see below).

Quants who are closer to the money (to the PnL) usually get a larger share of the profits. However, with this proximity comes the increased responsibility: who will lose their jobs first if the trading strategies don’t perform as well as expected?

There are quant jobs in asset management firms (the buy-side)

Usually the word “trading” is used to describe shorter-term, tactical decision making, whereas “investing” is reserved for longer-term, more strategic decision making. Professional investors tend to be called asset managers or portfolio managers (see our section on asset management jobs).

Portfolio management jobs are PnL-owning; portfolio managers are responsible for the bottom line. If their methodology is systematic (quantitative), rather than discretionary, they may also describe themselves as quants. Or they may be working with quants, who perform the analysis for them, but who don’t own the decisions and therefore don’t own the PnL. (See the description of a desk quant below.)

There are desk quant jobs

A desk quant supplements the trader/portfolio manager on a trading desk. Desk quants usually sit on the trading desk with the traders (whereas derivatives trading and model validation quants, along with technologists, often sit separately and may work in cubicles rather than on trading desks.) Different trading desks pay different levels of respect to their desk quants. Some desk quants are regarded as quantitative gurus; others simply perform the number crunching required by the traders and aren’t as important.

In each case the role of a desk quant is usually based on tighter schedules than that of a pricing quant and is seen as part of the front office.

There are quant jobs in risk management

People with quantitative finance expertise often serve not only as risk calculators but also as risk managers. Since the financial crisis, risk calculation has grown in importance relative to trading; it is seen as a critical supporting, non-revenue generating function.

Risk calculation involves not only quantitative talent, but also technologists, who build risk systems. The robustness of these systems plays an important role in the bank’s success (or otherwise) as a business.

Risk numbers used to take the form of VaR, CVaR and related metrics, which are heavily relied on to this day. After the global financial crisis  these metrics have been supplemented by various “valuation adjustments” that banks must make when assessing the value of derivative contracts that they have entered into. These are collectively known as X-value adjustments or XVA . The purpose of these is twofold: primarily to hedge for possible losses due to other parties’ failures to pay amounts due on the derivative contracts; but also to determine (and hedge) the amount of capital required under the bank capital adequacy rules.

The emergence of XVA has led to the creation of specialized desks in many banking institutions that manage the XVA exposures. These are regarded as separate from the traditional risk function.

There are quantitative developer jobs

Quants in financial services jobs produce vast amounts of code. This code may be in tactical (e.g. Jupyter notebooks needed to create and debug a model) or strategic (e.g. a derivatives pricing library). Depending on how strategic the code is, it must be written to different software engineering standards. Those who write code that will be run in production must be accomplished software engineers. Often, quants themselves have this skillset. Some of the best quants are often also some of the best coders. At other times, the less software-minded quants may rely on the help of quantitative developers , whose job it is to create (and debug) code rather than come up with new quantitative models.

So, what’s the difference between a strat and a quant?

The importance of quants in finance has been underlined by the renaming of quants to strats, which took place at several financial institutions. The word strat is an abbreviation for strategic analyst . The emphasis has shifted from the nature of the work (quantitative analysis) to its strategic role within the organization.

If you want to be a quant, however, you’re advised to look not at the title of a role but at its deeper nature. There are many quant jobs, differently named, with different strategic importance (and corresponding compensation).

Career paths for quants in finance

If you start working as a quant in a bank or fund, you don’t have to stay in that niche. You have other options.

For example, you could move into the financial technology (FinTech) industry. Fintech refers to the technology and innovation aiming to compete with traditional financial methods in the delivery of financial services. Some larger FinTechs are competing with established banks and hedge funds for quantitative talent. In particular this applies to non-bank liquidity providers.

You could also move to FAANG (Facebook, Amazon, Apple, Netflix, and Alphabet - formerly known as Google). Many FAANG firms hire quants to work on machine learning and artificial intelligence systems.

Not all quants are employed by banks, hedge funds, and other financial firms; some work in the academia. The pay is lower in academia, but the problems can be a lot more interesting. As you get more senior it can be possible to sit in both worlds, and to hold an academic job while working in a bank or fund at the same time.

For quants who want to publish research, there can also be opportunities to work on research desks, or for non-bank organizations that publis blue skies quantitative research. For example, Bloomberg has a sizeable research division, although they are not a trading firm.

Skills you’ll need for a quant job in finance

Traditionally, quants have had a background in applied mathematics of various flavours. Sometimes they come from the physics rather than mathematics, departments at universities. More recently, with the development of specialized quantitative finance education, pricing quants started to come from dedicated quantitative and computational finance programmes (such as the MSc in Mathematics and Finance at Imperial College, London, where I teach).

The mathematics you’ll need for quant jobs

Traditional Q-measure quant roles consisted in the (often numerical) solution of partial differential equations (PDEs) and stochastic calculus/analysis – the classical applied mathematics.

Such mathematics used to be taught at mathematics and physics departments of leading universities. Often quants came from relativity and string theory and fluid dynamics backgrounds – those areas where PDEs and stochastics abound.

After the GFC, P-measure jobs became more numerous. Such jobs relied more on statistics than on PDEs and stochastics. Accordingly, more people were hired with statistical rather than applied mathematics background.

The best degrees for quant jobs

In more recent years, dedicated mathematical finance programmes have been created at most leading universities. In addition to such programmes, which are usually delivered at the Masters level, it is nowadays possible to obtain a PhD degree in mathematical finance and/or complete a certification course, such as CQF.

The recent ML/AI revolution has further shifted the focus towards subjects traditionally regarded as computer science – the ML and AI. Dedicated programmes, such as Imperial College’s MSc in Artificial Intelligence, have been created in response to rising demand. Imperial’s MSc in Mathematics and Finance also includes a significant ML/AI component – a dedicated track. There are also certification programmes, such as the MLI.

The programming skills you'll need for quant jobs

Programming is as important to many quants as mathematics.

Quants who oversee quantitative libraries need to be well-versed in software architecture.

As well as writing code, quants spend their time debugging and speeding up existing code, creating quantitative infrastructure (eg. the way that different systems use to talk to each other, objects are persisted and stored, and interaction between the quantitative libraries and the underlying databases), automating tasks and – most recently – applying machine learning. Some leading financial institutions have dedicated machine learning teams. At others machine learning research or artificial intelligence implementation is conducted by regular quants. Banks, hedge funds, and trading firms are beginning to adopt new methods, such as deep pricing and deep hedging.

Which coding language do you need to learn if you want to become a quant? Modern coding languages have each their respective “ecological niches”:

• Python for prototyping and research;
• C++ for high-performance production systems;
• Java and C# for production systems where software engineering is somewhat more important than performance (although in some areas these languages compete with C++ for performance; see, for example, Azul);
• Julia attempts to combine the advantages of C++/Java/C# with those of Python;
• Kdb+/q and shakti for big and high-frequency data;
• CUDA for programming GPUs in high-performance computing (HPC) applications.

The soft skills you'll need for quant jobs

Quants don’t work in isolation. They collaborate (and sometimes compete and coopete) with traders, structurers, sales, technologists, risk analysts, and other quants. For this reason, the so-called “soft” skills are just as important as quantitative skills.

Senior quants often end up managing people and projects. People and project management expertise increases in importance as the quant’s career progresses, unless they choose to focus purely on the technical side of things, which is rarely possible.

Quants at various levels of seniority also have the task of convincing others of the usefulness and importance of the work that they do. As usual, there are many sceptics around, particularly when it comes to the latest approaches and technologies.

Pay for quantitative finance jobs

Given the huge variety of jobs on offer in quantitative finance, it’s hardly surprising that pay varies enormously . The eFinancialCareers salary and bonus survey shows that entry level salaries and bonuses for quants at banks in London are typically around £65k ($88k) plus bonuses of anything from £3.5k to £15k.  However, you’ll earn a lot more as a quant in a hedge fund.

Download our full salary and bonus survey here.  

Download our full guide to graduate careers in finance here. 

Contact:  [email protected]  in the first instance. Whatsapp/Signal/Telegram also available (Telegram: @SarahButcher)

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How To Get A Quant Job Once You Have A PhD

In this article we are going to discuss an issue that repeatedly crops up via the QuantStart mailbox, namely how to get a quant job once you have a PhD . There's a lot of confusion around this topic because quite a few people who currently work in academia and want to make the shift believe that it is quite straightforward to "walk into" a high-paying financial role. While this may have been true 10-15 years ago, the reality of the current job market is such that quant roles are now highly competitive and candidates need to stand out if they are to get the best jobs.

Firstly we'll discuss what sort of candidates you will be competing against when considering going for interview. Secondly, we'll discuss how to make an honest assessment of your PhD and what you got out of it that might be relevant to quantitative finance roles. Finally, we'll consider whether it is necessary to return to school in order to train up in a quant-specific qualification.

The Competition

I've made it rather clear on QuantStart that the competition for some of the top quantitative trading researcher roles can be extremely tough. In the UK the best roles tend to be filled well upstream of any "front door" interview process. Usually extremely bright academics in mathematics, physics, computer science, economics or mathematical finance are head-hunted for a particular skill set, such as deep expertise on market microstructure, insight into high-frequency trading algorithms, novel stochastic calculus techniques for certain derivatives pricing regimes or extensive statistical machine learning knowledge that applies to datasets used by such funds.

When such quant researcher roles ARE opened up to the public they will often state that they are looking for "only the best and brightest", which in the UK usually means "Top Five" universities (Cambridge, Oxford, Imperial College, LSE and UCL). In the US this will mean high-end Ivy League institutions. The adverts will often state that they want to see evidence of consistent Mathematical Olympiad prizes and an extensive publication list in a relevant field.

While this is certainly true of the top roles, there are plenty of other (very well paying and prestigious) jobs that also need filling. Bear in mind that there are only so many Mathematical Olympiad winners, after all! Thus one should not be disheartened when seeing numerous adverts asking for such qualifications. There are plenty of smaller funds and boutique outfits that do not have the resources to aggressively hunt for the ultimate talent and so will be more than willing to employ bright PhDs who might not necessarily have an Olympiad track record.

Honestly Assess Your PhD

The first task to carry out when applying for quant roles is an honest assessment of your PhD and what you achieved with it . Primarily you need to consider the level of mathematical ability you were able to attain as well as your computational programming skill.

Quant roles in the derivative pricing space, known traditionally as the "quant analyst" or "financial engineer", require a reasonable amount of mathematical sophistication. Specifically, expertise in stochastic calculus, probability and measure theory. These are topics usually taught in an undergraduate mathematics course, but can form a component of taught graduate school PhDs. In addition they require a good understanding of scientific programming usually in C++, Python or MatLab. Since the role of a quant analyst is often to code up an implementation of a particular algorithm from a research paper, under heavy deadlines, it is quite naturally suited to those with PhDs of this type.

Quant roles in the algorithmic trading and quant hedge fund world are almost exclusively going to require novel methods for generating "alpha" (i.e. excess return above a benchmark). Usually this is accomplished via time series analysis and econometrics, but more recently statistical machine learning techniques have been applied, as have methods related to sentiment analysis. Some of the best quant funds make extensive use of even more advanced graduate level mathematics in the realms of algebraic geometry, number theory and information theory. Hence anything highly mathematically, statistically or physically oriented is likely to be of interest to a top quant hedge fund.

As for computer scientists and strong scientific software developers, generally there is always work available for quantitative developer roles. Although you will be competing against those with industry experience in rigourous software engineering. Hence "academic code" of the "20,000 line single-file of Fortran" variety might be a bit of a hindrance! Make sure to brush up on the more modern software development methodologies such as OOP , Agile , etc.

I want to discuss specific PhD fields as well, to give you an idea of where you might consider focusing your efforts based on what you have previously studied:

• Pure Mathematics - The top funds generally hire the pure mathematicians from esoteric realms such as algebraic geometry and information theory. Banks will also take individuals who study stochastic calculus to a high level for their derivatives research teams.
• Mathematical Finance - Portfolio optimisation and derivatives pricing are two common themes studied in mathematical finance PhDs. You will often have collaborated with banks during your PhD, so it is unlikely your job prospects will be slim! If you are struggling, it can be very helpful to contact department heads as they will often have a strong network.
• Theoretical Physics - Funds will be very interested in your ability to model physical phenomena, either through direct or statistical approaches. Some theoretical physics areas are highly mathematical (Cosmology, String Theory, Quantum Field Theory etc) and so the advice given to theoretical physics PhDs is similar to pure mathematicians.
• Computational Physics/Engineering - The main skillset taught here is how to take an algorithm and produce a robust scientific computing implementation, perhaps in a parallelised fashion. This is an extremely useful skill for quant work both in banks and funds, especially for developing infrastructure. Make sure however to brush up on core topics such as statistics and stochastic calculus prior to interview.
• Statistics/Econometrics - Statisticians and theoretical econometricians will be in good demand from technical quant funds, especially in the Commodity Trading Advisor (CTA)/Managed Futures space. The time series modelling will be highly appropriate here.
• Computer Science/Machine Learning - Many funds are now making extensive use of machine learning and optimisation tools, which are the natural domain of the theoretical computer scientist and, more recently, the "data scientist". Familiarity with statistical machine learning and Bayesian methods will be highly attractive.
• Bioinformatics - Bioinformaticians also make extensive use of machine learning tools on "big data" sets. For interview you will want to emphasise your familiarity with such tools and your programming capability. Depending upon your background you may need to brush up on your (pure) mathematics for interview questions.
• Economics/Finance - Economics and Finance PhDs do not always teach you the mathematical maturity necessary for pure quant work, but it really depends on the project. You will need to be honest with yourself about where you lie on the mathematical spectrum. In addition you will need to consider your programming ability.

Heading Back To School

An extremely common question that I receive in the QuantStart mailbag is whether to return to school for finance-specific training subsequent to a PhD.

I've previously documented my views on Masters in Financial Engineering (MFE) programs as related to quantitative trading . In essence I believe that MFEs are not hugely suitable for quantitative trading research work, but they are a good entry point into investment banking quant work.

If your PhD was not heavy on quantitative or programming work, but you have a sufficiently mature mathematical background, then it can make good sense to take a MFE assuming that you can afford to fund the course. A MFE at a top-tier school will provide you with a solid network of other candidates (and thus people who might later help you secure a role), a relatively healthy recruitment position upon graduation and a useful skillset for investment banking derivatives pricing work.

I would advise against returning to school if you have a strong quantitative PhD as you simply won't need the additional qualifications and you should be able to pick up the necessary interview material yourself, albeit with a lot of study.

If you have a PhD in a non-quantitative field and your background is not sufficiently mathematical, then you should definitely consider that you will likely need to return to school if you truly want to work in quantitative finance. In particular you will need to study an undergraduate degree that has a strong quantitative component such as Mathematics or Physics as these two degrees will generally let you transition into other quantitative fields easily.

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