- 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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