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Quants: The Rocket Scientists of Wall Street

Quantitative analysts are professionals who understand the complex mathematical models that price financial securities and are able to enhance them to generate profits and reduce risk. As financial securities have become increasingly complex, demand has grown steadily for quantitative analysts , often called simply "quants," or even the colloquially affectionate "quant geeks."

Because of the challenging nature of the work—which needs to blend mathematics, finance, and computer skills effectively—quant analysts are in great demand and able to command very high salaries. Here's a look at what they do, where they work, how much they earn, and what knowledge is required, to help you decide whether this may be the career for you. 

Key Takeaways

• Quantitative analysts, or quants, combine their skills in finance, math, and computer software to analyze and predict the markets, creating complex models that can be used to price and trade securities.
• They tend to work in investment banks and for hedge funds, although insurance companies, commercial banks, and financial software and information providers may also hire them.
• Quants work in major financial centers in the U.S. and in London and Asia, among other places across the globe.
• Firms often look for candidates who have a master's degree or a Ph.D. in a quantitative subject, such as mathematics, economics, finance, or statistics.
• Compensation can be in the low-to-middle six figures.

Quantitative analysts design and implement complex models that allow financial firms to price and trade securities. They are employed primarily by investment banks and hedge funds , but sometimes also by commercial banks, insurance companies, and management consultancies; in addition to financial software and information providers.

Quants who work directly with traders , providing them with pricing or trading tools, are often referred to as " front-office " quants. In the " back office ," quants validate the models, conduct research, and create new trading strategies . For banks and insurance companies, the work is focused more on risk management than trading strategies. Front-office positions are typically more stressful and demanding but are better compensated.

The high demand for quants is driven by multiple trends:

• 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  

Quantitative analyst positions are found almost exclusively in major financial centers with trading operations . In the United States, that would be New York and Chicago, and areas where hedge funds tend to cluster, such as Boston, Massachusetts and Stamford, Connecticut.   Across the Atlantic, London dominates; in Asia, many quants are working in Hong Kong, Singapore, Tokyo, and Sydney, among other regional financial centers.

Despite the heavy concentration in those cities, quants are found all over the world—after all, many global firms analyze and/or trade complex securities, which creates demand for the quant's brainpower and abilities. But the problem that a quant working in Houston or San Francisco faces is that changing employers most likely would mean changing cities, whereas a quant working in Manhattan should be able to interview for and find a job within a mile or two of their previous one. 

What Do Quants Earn?

Compensation in the field of finance tends to be very high, and quantitative analysis follows this trend. It is not uncommon to find positions with posted salaries of $250,000 or more, and when you add in bonuses, a quant could earn $500,000+ per year. As with most careers, the key to landing the high-paying jobs is a resume filled with experience, including with well-known employers, as well as reliance on recruiting firms and professional networking for opportunities. 

The highest-paid positions are with hedge funds or other trading firms, and part of the compensation depends on the firm's earnings, also known as the profit and loss (P&L) . At the other end of the pay scale, an entry-level quant position may earn only $125,000 or $150,000, but this type of position provides a fast learning curve and plenty of room for future growth in both responsibilities and salary.

Also, some of the lower-paid quant positions likely would be primarily quant developers, which is more of a software-development position where the individual is not required to have as much math and financial expertise. An excellent quant developer could certainly earn $250,000, but that's about as high as the compensation package generally would go.

Despite the high pay level, some quants do complain that they are "second-class citizens" on Wall Street and don't earn the multimillion-dollar salaries that top hedge fund managers or investment bankers command. As you can see, financial success is always relative.

Estimated total pay of a quantitative analyst in the U.S. Google is among the 10 highest paying companies for a quant, offering an annual salary of $279,284.

Quants Skills and Education

Financial knowledge.

Many financial securities, such as options and convertibles , are easy to understand conceptually but are very difficult to model precisely. Because of this hidden complexity, the skills most valued in a quant are those related to mathematics and computation rather than finance. It is a quant's ability to structure a complex problem that makes them valuable, not their specific knowledge of a company or market.

A quant should understand the following mathematical concepts:

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

Key financial topics include:

• Portfolio theory
• Equity and interest rate derivatives , including exotics
• Credit-risk products

Some quants will specialize in specific products, such as commodities , foreign exchange (Forex) or asset-backed securities .

Computer Competency

Software skills are also critical to job performance. 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. A majority of the work is also realized in Python, as scripting-type languages are good for running lots of data and multiple scenarios.

Education and Certifications

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. Generally, an MBA is not enough by itself to obtain a quant position, unless the applicant also has a very strong mathematical or computational skill set in addition to some solid experience in the real world. 

While most financial certifications, such as the Chartered Financial Analyst (CFA) designation likely wouldn't add much value to a prospective quant's resume, one that may is the Certificate in Quantitative Finance (CQF) —which you may earn globally via distance learning in a six-month intensive program.

Clearly, you need to have "the right stuff" to be a quantitative analyst. It requires both the intellectual ability to master complex and abstract mathematical domains and a willingness to tackle challenges that can seem insurmountable—all while under considerable pressure—which only a select few can do.

But that also doesn't mean that everyone who has the ability to be a quant should become one. The financial problems that quants face are very abstract and narrow. Unlike fundamental or qualitative analysts , quants don't read annual reports , meet with management, visit operations, prepare roadshows, or talk to shareholders . Most of their time is spent working with computer code and numbers on a screen.

Individuals with strong analytical skills are valuable in many different areas of finance, such as economic and financial analysis , for example. Having to compete against the best and brightest quants every single day may not be the quickest path through the ranks, especially for those with broader skills and interests and a desire to manage.

Another career issue to consider is that many Ph.D. quants who come from academic environments find they miss the research environment. Instead of being able to study a problem for several months, when supporting a trading desk you need to find solutions in days or hours. This usually precludes making any breakthroughs in the field. 

Do Quants Get Paid Well?

Yes, quants tend to command high salaries, in part because they are in demand. Hedges funds and other trading firms generally offer the highest compensation. Entry-level positions may earn only $125,000 or $150,000, but there is usually room for future growth in both responsibilities and salary.

How Hard Is Quant Finance?

It take advanced-level skills in finance, math, and computer programming to get into quantitative trading , and the competition for a first job can be fierce. Once someone has landed a job, it then requires long working hours, innovation, and comfort with risk to succeed.

Do You Need a Ph.D. to Be a Quant?

Having a Ph.D. in a subject like math, finance, economics or statistics can be a definite plus for anyone wanting to become a quant. But a master's degree in computational finance or financial engineering can also be the ticket to a career as a quantitative analyst.

Success in quantitative analysis is largely based on knowledge, talent, merit, and dedication instead of the ability to sell, network, or play politics. The quants who work in the field are there because they can do the job well—an environment that many find remarkably refreshing—and they are justly rewarded for their work.

Bureau of Labor Statistics. " Financial Analysts ."

Open Quant. " The Various Types of Quants and Quant Employers ."

Bureau of Labor Statistics. " Financial Analysts: Work Environment ."

Glassdoor. " Quantitative Analyst Salaries ."

Bureau of Labor Statistics. " Financial Analysts: Pay ."

Glassdoor. " How Much Does a Quantitative Analyst Make? "

Duke University Career Center. " Quantitative Analysis ."

Bureau of Labor Statistics. " Financial Analysts: How to Become One ."

Certificate in Quantitative Finance. " Who Is It For? "

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The Quantitative Researcher Career Path

What does a quantitative researcher do and how can you land a job as one..

A career in quantitative research can be a highly rewarding and lucrative path for those interested in the financial sector. These professionals, often called "buy-side quantitative analysts", are typically found in investment funds and proprietary trading firms. Their primary responsibilites consist of conducting advanced research and analysis to inform proprietary investment and trading strategies. In doing so, they often leverage statistical and machine learning techniques.

To begin your journey into the field of quantitative research, you should start by building a robust base foundation in mathematics and statistics. While a bachelor's degree at a top-ranked unviersity may be sufficient for some entry-level positions, a master's degree or even a PhD is preferable. Some firms won't even consider your application if it doesn't meet their educational background requirements. In addition to a strong academic background, previous experience in a quant-related field is also highly valued by employers. This can come in many forms, such as an internship in quant research, data science, or machine learning. If you're currently looking for quant internships check out OpenQuant .

In terms of compensation, you can expect your total compensation to fall in the range from $200,000 to $250,000 . Depending on the specific firm you work at, this number can be even higher. While the stability of the job can vary, it is generally considered to be medium, as the demand for these professionals is high, but the competition is extremely fierce. There aren't many jobs on the market and internships are scarce. This is why having some educational or professional credentials can really stand you apart as an applicant.

The work-life balance of a quantitative researcher can also be arduous, as the job can require long hours and a high level of focus and attention to detail. The level of stress can also be medium to high, as the financial markets are constantly changing, and researchers must be able to adapt and respond quickly to new developments.

Despite the demands of the job, a career as a quantitative researcher is considered to be very prestigious, as these professionals are responsible for conducting research and analysis that inform important investment and trading decisions. There are also decent career progression opportunities for QRs, as many pursue roles in portfolio management or other leadership positions within the financial industry after a few years.

Quantitative researchers also tend to work very closely with quant traders and quant developers. Oftentimes, quant researchers will help to develop models and algorithms that traders use to make decisions and may also help quant developers with analyzing the results of their simulations. In some cases, the responsibilities of these roles may be more specialized, depending on the financial market and/or fund type.

Self-Study Plan for Becoming a Quantitative Analyst

This is part 2 in a 3-part series on how to self-study to get into quantitative finance. We've already covered self-studying to become a quantitative developer . In this article we'll look at forming a self-study plan to become a quantitative analyst/financial engineer .

Quantitative analysts and financial engineers spend their time determining fair prices for derivative products. This involves some deep mathematical theory including probability, measure theory, stochastic calculus and partial differential equations. Thus to become a quant analyst it is necessary to have a strong mathematical background in mathematics, usually through an undergraduate degree in mathematics, physics or engineering.

Undertaking self-study to become a quantitative analyst is not a straightforward task. Depending upon your background, aptitude and time commitments, it can take anywhere from six months to two years to be familiar with the necessary material before being able to apply for a quantitative position. However, the rewards are worthwhile. An extremely challenging intellectual environment coupled with highly attractive compensation provides strong motivation to work towards becoming a quant.

The Mathematical Fundamentals

Nowadays there is a comprehensive literature available for financial engineering. I've written many articles on this site about which books to start with, but I want to provide greater detail in this article as it is a study plan not a reading list!

• For those of you who are unfamiliar with financial markets or the derivative products within them, the best place to start is with John Hull's Options, Futures, and Other Derivatives . This is not a highly mathematical treatment of the subject, instead it concentrates on the different markets and products such as futures, options, swaps and other interest rate derivatives. I would suggest reading all of the chapters in this book, eventually, but only concurrently with a more mathematical text. Familiarise yourself with the chapters on futures markets, options markets, binomial trees, Wiener processes and the Black-Scholes-Merton model. Later on you can read about the "Greeks" and volatility. Hull is a great "bedtime" or "commuter" read, but you'll need something more mathematically-oriented to really get to grips with the options pricing material.

I would suggest that these books are sufficient to gain a good understanding of options pricing. If you know that you are going to become a fixed income quant, then you will obviously need to be extremely familiar with interest rate derivatives and models, such as Heath-Jarrow-Morton (HJM) and the Hull-White models.

If you wish to delve into more mathematical finance books take a look at the quantitative finance reading list section on mathematical finance .

Research Preparation

For some of you, getting a job in the financial industry is not the goal - you might want to pursue research in certain topics, either at PhD level or as a post-doctoral student, potentially coming from industry. The following books will give you a much deeper appreciation for options/derivatives pricing and will concentrate more on particular topic areas, such as fixed income or credit derivatives. You will almost certainly know your (approximate) research area before committing to a program. I've tried to provide some books which will give you a solid introduction to that particular area. By following the references, you will be able to learn more.

If you are simply interested in a career change into quantitative finance within industry or are after an entry-level role then feel free to skip this section and take a look at Programming Skills below.

Advanced Mathematical Finance

Advanced mathematical finance really comes down to learning more about stochastic calculus and risk neutral pricing. These are both extensive research areas in mathematics. The following books will give you a deeper flavour of what quantitative finance is about.

Topics (Fixed Income/Credit)

If your research area is geared more towards particular products - specifically in the fixed income and credit spaces - then the following books will be of interest.

Unfortunately I can't do justice to all of the highly interesting areas of research that financial engineering encompasses in this article, so I will have to stop there!

Programming Skills

Although you won't need to have as extensive a programming knowledge base as a quantitative developer , you will still need to have solid object-oriented programming skills, particularly in a language such as C++.

As a financial engineer you will spend about 50% of your time programming and implementing models. For that reason you will need to be familiar with C++ (or C#/Java) syntax, its pitfalls and "best practices". You will also need to be extremely competent at taking a mathematical algorithm and creating an object-oriented implementation that promotes maintainability, re-use and optimisation. These are difficult skills to learn unless you actually start implementing models. However, before we discuss numerical algorithms, we will talk about how to learn an object-oriented language, such as C++, to the extent necessary to perform well on a quant job (and pass an interview!).

Note that there will be some crossover here with the article on quantitative development , so feel free to look at that article for more details on programming.

• Once you've read the above two I would suggest taking a look at my own C++ book, C++ for Quantitative Finance . In it I cover some of the more intermediate C++ features and how, with some knowledge of design patterns, they can be applied to the problems that a quantitative analyst will face. The book is heavily geared towards in-depth implementations, rather than extensive theory, and will give you plenty to discuss in your quant interview.

If you wish to go further with your programming and learn about topics such as software engineering , version control and optimisation then take a look at the self-study guide for quantitative developers .

Numerical Methods

I have to admit that numerical methods are my favourite component of the financial engineering landscape. Further, they are possibly the most important part as well. Having a solid grasp of mathematics and stochastic calculus, while essential, means very little if you are not able to apply that knowledge to the practical pricing of derivative products. Generally one gains an education in scientific computing at PhD level or in grad school, as part of a computational/numerical PhD program. For those who haven't had a background in numerical methods, most likely due to a career change, it can seem like a daunting task to learn the material.

The best way to get started is to learn a fast language such as C++, as described above in Programming Skills , and then work through the books in the list below.

While the above may seem like a lot of material, you can break it down by avoiding many of the irrelevant algorithms. Concentrate on NLA, Monte Carlo and (maybe) some finite differences, as these are the cutting edge techniques. Remember though that you will only really gain experience via actually implementing these models. Make sure you program as many as you can to really get to grips with the material.

Interview Preparation and Final Thoughts

I've already written an article on interview preparation for becoming a quantitative analyst so I won't repeat myself too much here. Make sure you work through the five books described in that article and brush up on the myriad of brainteasers found within. They are an extremely common tactic to put a candidate under stress in an interview environment.

A strong investment in learning the material above well, coupled with extensive implementations of quant models in C++ along with practice interview questions from the above article will give you a very good chance of gaining a quant job in one of the top-tier firms.

Be aware though that it is a tougher market than usual for trying to find a quant position - particularly at entry-level. Investment banking interviews can be challenging. Thus it is extremely important that you study hard, implement the models and understand the basics thoroughly before applying to the recruiters.

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The Quantcademy

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Successful Algorithmic Trading

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Top quant PhDs are getting $400k pay packages to become e-traders

Several banks and hedge funds have increased the value of the compensation packages that they are offering to entry-level quantitative PhD graduates to become quantitative traders specifically involved in automated trading.

In fact, some banks offer entry-level quants with PhDs from top universities base salaries as high as $125k and hedge funds offer up to $175k base salary. Exceptional entry-level PhD quants can receive total compensation packages, including sign-on bonuses, worth up to $400k, according to recruiting firm Options Group.

While electronic sales-traders are vulnerable for headcount reductions, candidates with the right skill set receive, on average, 15% to 20% increases in total compensation to switch firms, with an upper limit of 25%.

"Banks continue to invest in improving the quality and depth of their electronic platforms while enhancing their reporting and compliance," says Push Patel, managing partner at Options Group. "The forecasted increases in compensation reflect the high levels of competition among firms to retain and recruit these professionals."

Ben Hodzic, a director at recruitment firm Selby Jennings, confirmed that those compensation figures are accurate for PhD graduates in finance, economics and econometrics coming from an Ivy League university with a Bachelor’s or Master’s degree in a STEM field such as engineering, statistics and computer science.

Banks and hedge funds are hiring quant PhDs right after graduation

Banks, hedge funds and proprietary trading firms are hiring quantitative researchers and quantitative developers to build and upgrade research platforms and work on optimization, while other quant candidates are tasked with contributing to alpha generation on a systematic trading desk, per Patel.

"Discretionary funds have also started to hire these entry-level candidates to create better screening mechanisms to help identify trading opportunities, utilizing data science instead of just pricing quants," he says. "Hiring entry-level quants are a part of larger programs to, in general, innovate for a wide variety of firms.

"Entry-level quants are not actually trading right out of school, but a few of them are involved in research that is applied to alpha generation," he says.

Financial services firms are hiring quants across the front, middle and back office

Maxwell Lennon, a VP of quant, tech and data (QTD) at recruitment firm GQR, says that PhD/Master’s graduates with STEM degrees are always in demand, but some of the best-performing programs he has seen for engineers are software management at Carnegie Mellon and computer science at MIT, as well as Oxford and Cambridge.

For quant researchers, they are usually coming from a mathematics or statistics background – the University of California, Berkeley, and Stanford for stats; MIT and Harvard for math.

Other quant feed schools include all Ivy League universities, Illinois Urbana, Michigan, NYU, Moscow Institute, ETH Zurich, Chicago Booth and the Indian Institute of Tech (IIT). Graduates from such schools go on to work as front-office pricing quants, quant traders, middle-office risk quants, software engineers, quant researchers, data scientists and, at Goldman Sachs, strats.

“Probably the greatest volume of quants that I see come from Master’s in financial engineering (MFE) programs at Cal-Berkeley and operations research at Columbia University. Lennon says. “An exceptional PhD graduate has a high GPA in math or stats and sometimes physics, but the hiring manager needs to prefer that, because there are some of teams that steer away from hiring physics grads considering their different approaches to solving problems.

“Other teams think they are the best, specifically for machine learning, but this is all a matter of opinion and a theory with a lot of detractors,” he says.

Exceptional PhD graduates enjoy multiple job offers

A PhD in a STEM field from a top 50 global university is the standard for being a desirable quant candidate, according to Options Group.

“Clients are looking for individuals who can be creative, technically hands on, most importantly, not theoretical,” Patel says. “Candidates from a top 50 global STEM program and an undergraduate degree with a high GPA are viewed more favorably than a mediocre undergrad and a strong PhD.

“Being ranked in the top 10% of the International Olympiads is also very desirable; however, what is key is the combination of being very quantitative and possessing sophisticated technical skills,” he says. “More recently, the demand to hire professionals with a background in artificial intelligence and machine learning is rising, and we expect this trend to continue.”

Finance PhD graduates from the University of Pennsylvania’s Wharton, the University of Chicago’s Booth, Harvard and a couple of other elite business schools are extremely sought-after for quantitative research, asset pricing, portfolio management/construction and strategy positions, per Hodzic.

“Within the finance field, there is heightened interest in research specific to asset-pricing techniques, as these are ever-evolving,” he says. “The schools listed here are great representations of where some of the original pricing models were founded, and have since been built upon using new modeling theory, statistics and programming to better fit the models and creates more optimized portfolios for clients.”

Rising pay as banks and hedge funds compete with other industries for talent

It’s not just banks that going after such candidates. An increase in data-driven strategies is ramping up the competition for talent, per Lennon. Hedge funds are also seeing this talent as rare, and are going above and beyond to pay a premium to secure them, per Hodzic.

“For candidates that have this [type of] PhD, a background or undergraduate degree in statistics or mathematics is seen as even stronger and worth paying a higher premium for,” he says. “These are small programs, so the best talent coming from each class is extremely selective and most of them are so well networked that they come out of school with three or more offers already in hand.

“This gives them a lot of leverage so other firms have to bid up to compete.”

Technology is evolving very quickly, and knowledge of new approaches and techniques and the ability to apply them are inherently a competitive advantage across many industries, per Patel.

“Every industry is involved in technology – from automobile manufacturers to pharmaceuticals to consumer and retail companies – so the financial industry is also competing with many other companies,” he says. “Part of it is simply a matter of supply and demand.”

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• Quantitative Finance Specialization
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• Management Science and Analytics (Ph.D.)

The Quantitative Finance specialization in the Ph.D. in Management Science and Analytics program is excellent preparation for either academic careers or for students who want to apply the theoretical, analytical, and quantitative rigor of management science to careers in finance.

Dissertation research in this area may include a wide range of topics such as risk modeling, financial time series analysis, and investment analysis.

Required courses for the Quantitative Finance specialization (three credits per course):

• MSC 621—Corporate Finance
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• MSF 545/MSC 613—Structured Fixed Income Portfolios
• MSF 546/MSC 614—Quantitative Investment Strategies

View the curriculum for the Ph.D. in Management Science (MSC) program and MSC course descriptions .

Career Opportunities

Industry and Research

The specialization in Quantitative Finance prepares students for a wide range of careers in finance, particularly in areas such as investment and commercial banking, trading, and risk management. This background also opens career opportunities across industries in business functions focused on finance, financial modeling, economics, and risk compliance.

Chicago’s position as a global center for finance and fintech, as well as the home to the world’s largest markets in financial derivatives, make it a prime location for internships, networking, and job opportunities for Stuart students in quantitative finance.

Our graduates are ready to step into roles such as:

• Senior quantitative analyst or quantitative analytics manager-economic modeling
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• Portfolio risk analyst, senior quantitative risk analyst, or exotic rates quantitative analyst
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Students interested in academic careers are supported by strong mentoring relationships with our faculty, opportunities to co-author papers published in prestigious scholarly journals, and help in securing adjunct positions to develop their teaching skills.

As a result, our graduates have launched teaching and research careers as finance faculty members at colleges and universities in the United States and around the world, such as:

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Quant hedgefund vs. consulting firm after PhD?

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I am a PhD student graduating this year from a Quantitative background. I have taken financial engineering classes and statistics classes. And since I have done a master somewhere else, so I am relatively older than other PhD graduates.

The natural places to go are the Quant hedgefunds or Quant prop shops. They hire a lot of high frequency quant analysts these days. I could also potentially land a job as sell-side Quant in a bank, since I had internship experiences. But those jobs are boring.

However, I feel in these jobs I face only cold computer screen programming everyday which will make me more and more nerdy. The reason is that in order to be successful in this field, you have to spend after-work hours thinking of your trading strategies and programs, which is still facing the computer. I desire to have a job that has more flavor of interacting with people. Interacting with people and making friends generally make life happier.

Ultimately I strongly desire to be an investment guru and fund manager. It looks like PE shops are a comparable career destination. However, PE requires the smart MBAs , not the dumb PHDs. It's impossible for me to step into PE at this moment. The only possible way of getting a job that has more human interaction is to get a consultant job. There are consultant firms that at least hire some PhDs. Hopefully the consultant job could potentially lead me into the PE world. I am also pursuing a CFA , and hopefully I could avoid doing an MBA , because including the preparation and application time, etc. getting an MBA at mid-30s is no fun! :=(

So my questions for the experts on this board are:

• Is the consultant route the best way for a PhD to go to PE?
• Will the consultant + CFA allow me to avoid the mid-step of MBA?
• What type of consultant role will be the closest to PE?

Please kindly shed some lights on me. Thanks so much in advance for your enlightening opinions and suggestions.

wow you have the perfect background to get into derivatives trading or structuring (when the dust settles)- also, quants in exotics who build the pricing models work on the trading floor so there's alot more interaction with other people than you think (plus the pay for quants is stratospheric at senior levels, on par with MDs in trading )... basically everyone on Wall Street stares at a computer screen the better part of the day, so programming will not make you antisocial- even though debugging is incredibly annoying, my sense is that writing code is one of the most creative and challenging jobs there is (in addition to graphic designers, musicians, etc.)...

Consulting is a broad field and I'm not sure if you're thinking management consulting (e.g. McKinsey , Bain, BCG ) or economic consulting (e.g. NERA, Brattle, Cornerstone, etc.) Economic consulting has a lot more PhDs and the work is probably more challenging (a lot of econometric modeling). But working in litigation , on behalf of lawyers, is boring. I know McKinsey hires a bunch of PhDs for their New Jersey office, but I don't really know what PhDs do over there or why one would need advanced scientific training to put together power point presentations and do telephone surveys.

The quant field is undoubtedly the future of finance and you already have the premier designation to work in the field- I would think the barriers to entry are high for others and if you can pair that with strong business skills (like the CFA ), you'll be on your way to an interesting, high paying career.

Thanks a lot MichaelHutchens for your warm encouragement. I agree that an exotic derivative trader or a structurer could be a possible route. But first of all, I doubt exotic will come back again in the next 5 years and I don't have 5 years to wait. Secondly, the job doesn't really make social impact and is far from business. It's more interesting to help a weak company turn into a strong company, and to turn a good company into a great company, which "supposedly", could be done thru Private Equity. This is the social return side. From the personal return side, you also have carry interest, which could be more cash than being a sell-side guy, as well as meeting with more people, which generally makes life happier. In the far future, with the business skill we've accumulated, we could open our own business and explore many fun ideas.

I am not sure how to acquire these types of skills thru being a derivative trader or structurer.

Are you saying that a sell-side derivative trader or structurer role could also eventually lead to a buy-side PE role?

have you considered being a quant trader?

timergooff: have you considered being a quant trader?

Yeah, there are quant trader positions in a bunch of quant hedgefunds, banks, and prop shops. The high frequency trading recently became the buzzwords. However, this is really the programming work. I admit that I equally love programming&math, exploring trading strategies, as well as interacting with people. However, to really succeed in this trading strategy arm race, we will have to spend 14 hours a day staring at computer screen, and thinking hard about improving trading strategies. I think these types positions have even smaller human interaction than the sell-side trader and structurer.

Why did you get a PhD if you have no interest in the field?

drexelalum11: Why did you get a PhD if you have no interest in the field?

I enjoyed my PHD and enjoyed the time spent in front of computer and doing math. But now looking back, I wanted to get more human interaction and more business side, nothing wrong with that, right?

yea, nothing wrong with your wanting to, but what matters is your being able to. who's gonna hire an engineering Ph.D. for interacting with people?

I think if you want to go into the business side, working in S&T in banks or HF is not the way to go. They are completely different jobs and require different kinds of skillset

I'd say the best option now is to try to get into strategy consulting as part of their PhD intake. Going to PE after consulting should be a natural route.

where are there good quant phd programs?

Getting a PhD in math or comp sci is brutal. Unlike law or business schools, or masters degrees, getting PhD in these fields require a strong background, good recommendations from professors, and possibly some research experience or publications. One can graduate from college, work a year and then decide to go to business/ law school . There is no such luxury for getting a math PhD from Princeton. You'll prob have to decide that's what you want to do by junior year and work towards getting admitted.

The good programs are at the top schools in the country. For math, Harvard, Princeton, Chicago, MIT, Stanford, Berkeley etc. Largely similar list for comp sci, you can add CMU, Caltech. I've probably left out a few really good schools. But it's hard.

Your best bet would be to work for a Growth Equity fund in tech or biotech. The people-facing, business-improving work the OP was seeking will at least match the level you would find in PE . Also, PhD's of all technical varieties are generally prized in VC /Growth funds.

did you decide are you still deciding? i think your expectations are pretty off ... quant HF do not spend 14 hrs a day grinding like IB . whereas most PE types probably do spend 14 hrs a day grinding away on excel spreadsheets not the wheelin and dealin u might imagine. likewise consultants probably spend a lot of time front of the computer too. also your job probably won't solve your social life. hope you did some more DD.

Why did you get the PhD if it sounds like you want to do consulting....?? I recommend going into the quant groups at management consulting firms, BCG and Bain have them.... they do statistical modeling for clients

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• Career Advice

PhD in physics, first job as a quant

• Thread starter feynmanjr
• Start date 3/30/16

I am a PhD student in physics in US and I am going to graduate by end of this summer. Recently, I have decided to shift my career toward a quantitative job instead of going to a postdoc. I have read some basic books in finance and I am good at mathematics and I worked with C++ / python and mathematical softwares (Mathematica and Matlab), but it was mostly through self study in school. I want to know what my chance is for applying for getting a job as a quantitative analyst or developer (or similar jobs). Do I have to invest in getting a MFE or some similar degree before thinking of applying in that area? Also, since I am going to graduate in less than a year, is there anything that I can do to help my condition (finding headhunters or studying some special topics)? -thanks  

IntoDarkness

wow, quitting academia but still call urself feynman jr...  

Since you have a very short time frame, a useful materials for a PhD quant wannabe is: A Practical Guide To Quantitative Finance Interviews by Xinfeng Zhou (Author) 150 Most Frequently Asked Questions on Quant Interviews by Dan Stefanica Rados Radoicic (Author), Tai-Ho Wang (Author) Quant Job Interview Questions and Answers (Second Edition) by Mark Joshi (Author), Nicholas Denson (Author), Andrew Downes (Author)  

Thanks @rnavarro . I will definitely Start reading these books. Also, do you have any suggestions how to find headhunters or should I upload my CV directly in any online job ad that I see? @IntoDarkness : Feynman Jr was my nickname in more than a few years and I usually use it for my username.  

Networking is the key. You can also start with headhunter site such as selbyjennings, huxley, and efinancialcareers. Don't forget to network via LinkedIn. It is very tough to break into Quant Finance but it is worth it. Good Luck!  

ShowMeTheLight

The main thing I would stress is not to get too cocky regarding your math, C++ or quantitative finance skills. The top hedge funds will test all of these like nothing you ever experienced in grad school. They want to make sure you know everything in C++ inside out, upside down and backward. Honestly, after a Physics PhD, it might still take you 6-12 months to prepare for the interviews at the TOP places. I would pick up all of the recommended books, plus Elements of Programming Interviews, and work through the problems carefully.  

The suggestion I gave was more of a quick notice preparation for those graduating few months from now. I know several people taking their PhD in Physics either took for MS Statistics degree or get some Math Finance courses ( C++ programming included) the year before they graduate. In addition, experience with large data sets (such as using Machine Learning, Time Series, Market Microstructure) is desirable.  

I agree with both of you ( @ShowMeTheLight and @rnavarro ). Unfortunately, I have not prepared myself enough during my phd for such a job. If I had time, I would also consider a couple of internship in different places in previous summers). I know that probably I will not get an offer from any of top hedge funds/Investment banks, but I want to start my first job at this point. I know C++ /math/statistics, but I do not know them enough to pass any kind of tests. I have learnt them (mostly coding and statistics) by myself, so I emphasized on the parts that I needed. My advantage is that I am relatively good in all of them and I am willing to learn whatever I need to know. @ShowMeTheLight : I would not mind spending some time to learn these, but I am mostly concerned about not having any experience. Do you think I should forget about applying until I completed all different books that are in that area?  

feynmanjr said: I @ShowMeTheLight : I would not mind spending some time to learn these, but I am mostly concerned about not having any experience. Do you think I should forget about applying until I completed all different books that are in that area? Click to expand...

ShowMeTheLight said: Yes, definitiely do not apply until you have put in 6-12 months of study in C++ especially, as well as probability, statistics and even basic machine learning and databases (SQL, Hadoop, HBase). If you apply too early, you won't make it past the 30 minute phone interview. They will just cut you when you say you don't know how to release a unique_ptr or how to write a copy constructor, or overload the () operator. Click to expand...

@ShowMeTheLight : I am not going to apply if it says "strong C++ skills", since all my knowledge is based on my personal learning. However, I can write a program and I can easily find my way, if I face a problem that I have not seen before (probably not useful for interview). Also, my main strength is in math (mostly statistical mechanics and PDE/stochastic calculus) and I am hoping to get a job that requires both of these skill at the same time. I will follow your advice and try to become a master in those areas. @pingu : I agree that even if I fail at interview, it can help me for my next ones. I do not think I can be qualified as a person who works with extremely large databases.  

Finder of biased coins.

pingu said: I have a different opinion. Apply to all jobs and use it as experience or training. Think like you are practicing a sport and getting better at it. Click to expand...

feynmanjr said: @ShowMeTheLight : I am not going to apply if it says "strong C++ skills", since all my knowledge is based on my personal learning. However, I can write a program and I can easily find my way, if I face a problem that I have not seen before (probably not useful for interview). @pingu : I agree that even if I fail at interview, it can help me for my next ones. I do not think I can be qualified as a person who works with extremely large databases. Click to expand...

@ShowMeTheLight : I agree. I am not planning to act line I am a genius, so they should hire me. As you said, most of the mathematical modelings happened when Black-Scholes model came out(70s-90s). I can write any program in C++ and I know some other languages like python . The point is I have learned all of these by self study, so I may not know exactly what unique pointer or other expressions means. I have also worked with SQL databases when I was designing software and web pages a while ago, but it was also self study (I can write a code to do the job, but I may not know the official name for anything that I use). My plan is to do math and computer programming and I doubt if I can compete with someone whose major is computer science. As a person with no experience, I do not think I can get a job at somewhere like Goldman Sachs, so following your advice, I am going to learn more expressions in C++ and then start applying. Since I am going to graduate this summer, I am willing to get any job I can get (or maybe a part time job first). If I do not get a job, I may try to go to a postdoc and find a job after that.  

Daniel Duffy

C++ author, trainer.

My advantage is that I am relatively good in all of them. How did you determine this?  

feynmanjr said: @ShowMeTheLight : I can write any program in C++ and I know some other languages like python . Click to expand...

Here is a list of commonly asked C++ interview questions. This should keep you busy for sometime and you will not be blindsided in the interview: C++ Interview Questions and Answers - Test & Download http://www.cs.ucr.edu/~lyan/c++interviewquestions.pdf Google You also need to go through the book "Cracking the Coding Interview" Get it from Amazon.com  

I see. I put a bad wording by saying I can write anything. I definitely cannot. In fact I do not know if I know enough for a specific position or not. I meant I can write a program to find noise in a 1-milion line code, or I can sole a partial differential equation (or some simulations). I am definitely not an advanced programmer. @ShowMeTheLight : I understand that you are frustrated that physicists who say things like that. I did not want to look like a very arrogant person who think he knows anything. I am pretty sure that most of the things that I learned in school is not going to be useful in finance. @Daniel Duffy : I did not mean I am pretty advanced in C++ . I will definitely be more careful in future in my statements. In fact, that is exactly why I want to go to industry, so I can get a better understanding of how to deal with a real problem. @TraderJoe : thank you for your suggestions. I will read them before going to an interview.  

feynmanjr said: @ShowMeTheLight : I understand that you are frustrated that physicists who say things like that. I did not want to look like a very arrogant person who think he knows anything. Click to expand...

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UMD Ph.D. Student Snehesh Shrestha's Software Uses AI to Teach You How to Play the Violin

On the ground floor of one of the new computer buildings at the University of Maryland, Anna Kelleher played her centuries-old violin while a program running on a laptop in front of her told her to do things such as raise her chin or widen her stance.

These were common mistakes that Kelleher knows not to do. After all, she’s a graduate student studying violin performance. But she also teaches violin to others, and the program she was demonstrating might someday help those she teaches to play even better.

Believers in artificial intelligence say the program will radically transform our lives in so many ways.

It’s designed by Snehesh Shrestha , a Ph.D. candidate at the Department of Computer Science, and is the perfect example of how the University of Maryland is building bridges between AI and every other academic program on campus.

A simple webcam found on your laptop, or even your phone, captures enough movement and audio from your performance that the AI program can tell what you’re doing wrong. Whether your stance is too wide or narrow, to whether or not your chin is in the right spot, it can see and also hear everything you’re doing right and wrong.

The program was designed to try “to understand the whole space, not just blindly building a technology, but understanding how can we fill the gaps that are currently there in the entire music learning process,” Shrestha said. “And by identifying gaps where we can empower the teacher and the students, we could really build something a lot more powerful than just building a single technology. And that really was like the starting point of exploring into what the technology can provide towards the future direction of music education.”

On the monitor, the teacher can see the student in 3D — every angle imaginable — to see how they stand and how they move. Technology, including a piece that looks sort of like a smartwatch, can also send cues to the student through vibrations in the wrist.

Click  HERE  to read the full article

The Department welcomes comments, suggestions and corrections.  Send email to editor [-at-] cs [dot] umd [dot] edu .

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