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My mom, my friends, and students often ask me: “What exactly is a quant?”
If you look it up online, you’ll find all sorts of definitions—some overly broad, others too technical. But at its core, a quant is someone who uses mathematics, statistics, and computer science to solve problems in finance.
That’s the fundamental idea.
Where it gets more interesting—and more confusing—is in the wide range of domains where quants apply their skills. Being a quant doesn’t mean doing the same job everywhere. In fact, the day-to-day work can vary dramatically depending on the type of firm, the asset class, and the specific function.
Here are just a few examples of what quants might do:
At an investment bank’s sales and trading desk, a quant might develop pricing models for complex structured products.
At a commercial bank, a quant could build credit risk models to predict whether a client will default on a 30-year mortgage.
At a commodities hedge fund, a quant might use computer vision to estimate the depth of an oil tanker in the water—and infer how much oil it's carrying to speculate on supply-driven price movements.
At a high-frequency trading (HFT) firm, a quant could be designing algorithms to optimally place bids and offers in milliseconds, balancing profit potential with inventory risk.
At a brokerage, a quant might research execution algorithms to figure out how to route client orders across venues to minimize transaction costs and market impact.
All of these roles fall under the umbrella of “quant,” but the problems they tackle—and the skills they emphasize—can be vastly different.
In short, a quant is not really a job title. It's a way of approaching financial problems through the lens of data, models, and algorithms.
In the finance industry, quants are often informally divided into two broad categories: P-Quants and Q-Quants. These aren’t official job titles, but rather industry jargon that captures two fundamentally different approaches to quantitative work:
P-Quants focus on predicting the future
Q-Quants focus on pricing in the present
If these terms sound a bit abstract, don’t worry—they’re just common industry shorthand. What matters most is understanding the underlying difference in mindset and application.
A quick rule of thumb:
P-Quants are typically found on the buy-side (e.g., hedge funds, asset managers, proprietary trading firms)
Q-Quants are more common on the sell-side (e.g., investment banks)
Let’s break each down.
Q-Quants: "Extrapolate the Present"
Q-Quants work primarily on the sell-side, especially within investment banks. You’ll often find them embedded on sales and trading desks, particularly in structuring or exotic derivatives teams.
Their core task is to price complex financial products in today’s market, using mathematical models that assume no arbitrage—in other words, that all risks can be perfectly hedged. They're not making market predictions; they're determining what a product is worth based on current market conditions and how it can be replicated or hedged using other instruments.
Think of a Q-Quant like an insurance underwriter: their job isn’t to predict the next disaster, but to set a fair price today for a contract that accounts for all the risks—and ensures the provider (the bank) is fully protected.
Technical Detail:
The “Q” in Q-Quant comes from the Q-measure (or risk-neutral measure) in financial mathematics. Under this framework, prices are derived assuming all risks are fully hedgeable, and expected returns are adjusted for risk.
Example:
A Q-Quant helps structure a bespoke options product for a client.
The bank doesn't want to speculate—it wants to sell the product, hedge the risks, and collect a fixed fee (often ~1%).
The Q-Quant builds a pricing model to determine the fair value of the product and to ensure the trade can be hedged with existing instruments.
Their goal is not to “beat” the market—but to ensure consistency, hedgeability, and profitability through fees.
Skill Emphasis:
Q-Quants typically lean more heavily on mathematics, particularly stochastic calculus, partial differential equations, and closed-form solutions in pricing models.
P-Quants: "Model the Future"
P-Quants, on the other hand, are usually found on the buy-side. They work at hedge funds, quant asset managers, or proprietary trading shops. Their job is fundamentally different: they aim to forecast market behavior and identify profitable opportunities—a.k.a., “find alpha.”
Think of a P-Quant like a weather forecaster: their job is to make the best possible prediction of future conditions, and position accordingly.
Technical Detail:
The “P” in P-Quant refers to the P-measure (or physical probability measure)—which reflects real-world probabilities. P-Quants model how the market is actually likely to behave, and seek to profit from mispricings or inefficiencies.
Example:
A P-Quant builds a statistical model that predicts which stocks are likely to rise over the next month.
If the model identifies a stock that appears underpriced relative to its risk, the fund may take a long position.
If the prediction is accurate, the fund earns a profit.
P-Quants are also involved in risk modeling, portfolio construction, and signal combination—any task where future distributions matter.
Skill Emphasis:
P-Quants tend to focus more on statistics, data analysis, and machine learning.
The Line Can Get Blurry
While the distinction between P-Quants and Q-Quants is conceptually helpful, in practice, the lines often blur.
For example:
A volatility trader may hedge current market risks using Q-measure models (pricing and replication), but also use P-measure models to forecast volatility and place directional bets.
A statistical arbitrage quant might assume no-arbitrage pricing relationships (Q-thinking), while simultaneously betting on mean-reverting behavior or momentum (P-thinking).
Many successful quants learn to navigate both worlds, applying the right tools depending on the objective—hedging versus forecasting, pricing versus trading.
Resources
P vs Q Quant by Dimitri Bianco
"P" versus "Q": Differences and Commonalities between the Two Areas of Quantitative Finance by Attilio Meucci
P Quant or Q Quant, Which One is the Future by Hang Li
If you have ever browsed job postings in quantitative finance, you've probably come across three common titles:
Quant Researcher (QR)
Quant Trader (QT)
Quant Developer (QD)
I don't like these titles. Depending on the firm, the same title can mean vastly different things. So the first takeaway is: these titles are ambiguous and don't really matter.
Let's take Hudson River Trading (a leading proprietary trading firm) as an example. They have the roles:
Algo Developer: Focuses on building and maintaining the models that drive trading strategies.
Core Engineer: Focuses on building the infrastructure and tools that support the Algo Developers.
At a different firm, HRT's Algo Developer might be called a QR or a QT. So even though the work is the same, the title might be different.
With that in mind, I'll attempt to describe what these roles typically mean at buy-side institutions (i.e., hedge funds and proprietary trading firms, not sell-side firms like investment banks).
At their core, Quantitative Researchers are model builders. They’re the ones who design, test, and refine the mathematical and statistical frameworks that drive trading decisions. Whether it’s predicting market movements, optimizing execution, or analyzing new datasets, the QR’s job revolves around turning data into actionable insight.
At Prop Trading and Market-Making Firms
(E.g., Citadel Securities, Jane Street, Jump, HRT)
These firms operate in high-frequency trading (HFT) and blend strategies: market-making, relative value, directional bets, etc.
A QR at these firms might work on:
"How should we optimally place orders into the order book?"
"Can order book imbalance predict short-term price moves?"
They may also work on projects that are less directly tied to PnL but are still impactful, like optimizing neural network training or simulation engines.
At Hedge Funds:
QRs show up in multiple types of teams:
Fundamental Pods at Multi-Strategy Funds
(E.g., Citadel, Millennium, Point72)
A fundamental pod typically revolves around a discretionary portfolio manager (PM) who makes investment decisions based on fundamental analysis—like company earnings, management quality, supply chain insights, or macroeconomic data.
The PM drives the investment thesis.
The QR plays a support role, helping validate or refute the PM's hypotheses using data.
Example: “Does credit card transaction data predict earnings surprises for retail companies?”
The QR would clean the data, design statistical tests, and simulate portfolio outcomes.
The QR may also build tools for monitoring factor exposures, risk, and performance attribution.
The QR is not typically the risk taker in this setup—the PM is.
Systematic Pods
(E.g., Two Sigma, D.E. Shaw, Cubist, Citadel GQS):
A systematic pod is focused on fully automated, data-driven trading. Here, decisions are not made by human discretion but by algorithms developed and backtested by the team.
The QR is the primary driver of performance.
They develop alpha signals (predictive indicators).
Design portfolio construction and risk models.
Optimize execution algorithms.
Often, they work closely with QDs to productionize strategies and with traders (if any) for real-time adjustments.
These pods are self-contained systems where the model is the strategy—and the QR is the brain behind it.
In this setup, even though the role is labeled Quantitative Researcher, it overlaps heavily with what some firms call Quantitative Trader—since the QR is directly accountable for the PnL of their strategy.
Quantitative Traders are the decision-makers at the interface between models and markets. Their job is to take risk intelligently, using tools and signals developed by Quant Researchers and implemented by Quant Developers.
While QRs focus on designing models, QTs focus on executing them—often in environments where market conditions shift quickly and human judgment is still critical.
At Prop Trading and Market-Making Firms
(E.g., Citadel Securities, Jane Street, Optiver, Susquehanna)
These firms often operate at various frequencies—from ultra-high-frequency to intraday to medium-term—and many specialize in market-making or options trading, where real-time decision-making is still vital.
QTs at these firms:
Monitor markets in real time and adjust strategies based on short-term dynamics.
Use dashboards and interfaces developed by QDs to interact with trading systems.
Tweak model parameters when unexpected events occur (e.g., earnings shocks, geopolitical events, pandemics) that invalidate assumptions embedded in models trained on historical data.
Here’s how I like to think about it:
In Formula 1, there’s the car and the driver.
The QR is the car.
The QD is the mechanic.
The QT is the driver.
For example, a QT might work with a dashboard displaying a volatility surface—a model-derived internal “fair value” of an option—but adjust certain parameters (like uncertainty) during volatile periods.
QTs are typically:
The primary risk takers on their desk
Responsible for managing capital, executing trades, and managing exposure
Key collaborators with QRs and QDs, feeding back insights from market behavior to inform model or tooling improvements
Occasionally doing their own lightweight research or event studies, such as:
“What usually happens to implied volatility after a Fed announcement?”
At Hedge Funds
The title “trader” at a hedge fund can be more ambiguous—and often means something quite different depending on the team.
Risk-Taking Traders
Some hedge funds (especially systematic ones) have QTs who take on a role similar to those at prop firms—adjusting models, managing risk intraday, and helping bridge the gap between research and real-time execution.
Execution Traders
On the fundamental discretionary side, however, many “traders” are actually execution traders:
They don’t take risk themselves.
Instead, they are responsible for efficiently executing orders sent down from portfolio managers.
Their job is to minimize market impact, ensure best execution, and sometimes engage with brokers or venues to source liquidity.
So while QTs at prop firms are often the brains behind risk-taking decisions, execution traders at hedge funds may simply be facilitating trades—very different responsibilities despite the same title of trader.
Quantitative Developers are the engineers behind the systems. Their job is to turn research ideas into reality—building the infrastructure, tools, and codebases that power modern quantitative trading strategies.
While QRs generate the ideas and QTs interact with the market, QDs make it all run reliably, efficiently, and at scale.
At Prop Trading and Market-Making Firms
(E.g., HRT, Jump, Citadel Securities, Jane Street)
At these firms, speed, reliability, and system design are often the competitive edge. QDs are responsible for building and maintaining the low-latency, high-performance infrastructure that enables trading strategies to function.
Key responsibilities may include:
Writing ultra-low-latency code in languages like C++ or Rust to support HFT strategies
Managing network stack optimization, hardware acceleration, and co-location setup
Designing and deploying real-time data ingestion pipelines and order management systems
Collaborating with QRs to productionize models, and with QTs to build intuitive dashboards or control interfaces
A QR might test a trading signal in Python using a notebook. The QD turns it into fast, reliable C++ code that runs in production—complete with unit tests, monitoring, and fail-safes.
In many HFT firms, QDs are deeply technical and highly respected—often contributing directly to the firm's edge through engineering innovation.
At Hedge Funds
The role of a QD at a hedge fund is often broader and more diverse, depending on the team’s structure and focus.
Tool Builders and Infrastructure Engineers
QDs build internal tools used by:
QRs (e.g., for research, simulation, backtesting)
QTs (e.g., for trade monitoring, execution dashboards)
Portfolio managers (e.g., for exposure tracking, scenario analysis)
They ensure systems are:
Scalable (able to handle terabytes of alternative data)
Stable (robust to edge cases and market anomalies)
Secure (with proper access control and audit trails)
Productionization of Research
QDs often serve as the bridge between research prototypes and live strategies:
Take a backtest written in pandas → convert it into a distributed Spark pipeline
Migrate a signal from a notebook → into a production execution engine
Add logging, alerting, CI/CD pipelines, and performance tuning
Information Barriers
In some firms, especially larger multi-manager hedge funds, QDs are deliberately kept at an arm’s length from sensitive trading logic. This is to maintain information barriers between infrastructure and trading.
These QDs might not know exactly what is being traded, or why a strategy works.
Instead, they focus on system integrity, performance, and usability.