Quantitative Researcher

Also known as: Quant Researcher, Quantitative Analyst, Quant, Alpha Researcher, Systematic Trading Researcher.

What does a Quantitative Researcher do?

A Quantitative Researcher is a researcher who builds models on market data to make money from trading or to manage risk. They work with noisy data where most patterns found turn out to be chance, so rigorous validation is the core of the job. They take a finished strategy to production together with developers and traders. Typical tasks:

• form hypotheses and look for trading signals in prices, volumes and alternative data;
• run backtests and check strategies for overfitting and look-ahead bias;
• build models for pricing instruments, risk and trade execution;
• estimate a strategy's capacity and execution costs;
• monitor live strategies and decide when a signal has stopped working.

When does a company need a Quantitative Researcher?

A company needs a Quantitative Researcher when it makes money in the markets systematically and wants to find new sources of return or improve existing strategies: trading firms, market makers, hedge funds, crypto exchanges, trading desks at banks. Common triggers: strategies have started to earn less, the company is entering a new market or asset class, the team depends on one or two researchers. Without data, backtesting infrastructure and execution, a researcher cannot take ideas to live trading, and then Quant Developers and Data Engineers are needed first.

Quantitative Researcher vs Quant Developer: what's the difference?

A Quantitative Researcher differs from a Quant Developer in deciding what to trade and why, while a Quant Developer builds the trading infrastructure and makes strategies fast and reliable. In a strong team they work as a pair, and in high-frequency trading the line between them is blurred. What sets a quant researcher apart from a Data Scientist is the cost of a mistake: with product data a mistake means a worse metric, in the markets it means losing money the same day.

Why is it hard to hire a Quantitative Researcher?

Quantitative Researchers are hard to hire because the market for them is small, and strong researchers are protected both financially and legally.

• Past results cannot be shown: strategies belong to the employer, and CVs contain almost nothing specific.
• Non-competes and long notice periods can last months, so the move to a new employer has to be planned well in advance.
• A candidate's decision depends heavily on how the bonus is calculated and tied to PnL, and companies arrange this very differently.
• Researchers with an olympiad or academic background and real trading experience rarely look for jobs themselves.

What to decide before hiring a Quantitative Researcher?

• Which markets and trading horizon? High-frequency, intraday and medium-term trading require different skills, as do equities, futures, options or crypto.
• Do you need someone for new strategies or to improve existing ones? The first needs a lot of freedom and patience from the company, the second needs deep knowledge of a specific market.
• Is there data and research infrastructure? If not, the researcher will spend months building it instead of looking for signals.
• How are results measured, and how is the bonus tied to PnL? An opaque scheme puts strong candidates off at the first conversation.
• What will the candidate be unable to bring from their previous employer? Non-competes and intellectual property rights matter more here than in almost any other role.

Where to find a Quantitative Researcher?

Quantitative Researchers most often come from trading firms, market makers, hedge funds, crypto exchanges and bank trading desks; many started in mathematics, physics and computer science. A strong profile signal is olympiads, academic publications or data science competitions combined with experience in specific markets. NEWHR finds such candidates by the results of their work, not by applications to a job post.

How to assess a Quantitative Researcher in an interview?

Quantitative Researchers are assessed with probability and statistics problems, followed by a research case: how to validate a signal, how to avoid look-ahead bias and overfitting, what counts as evidence. A past piece of research is reviewed without disclosing the previous employer's strategies. On the company side, the Head of Research and, for senior roles, a partner or Head of Trading take part.

Strong signals:

• looks for reasons why the signal they found might be chance;
• talks about costs, capacity and how a strategy stops working;
• carefully avoids details of previous strategies and still explains their reasoning.

Red flags:

• shows an impressive backtest without out-of-sample testing;
• is willing to share details of a previous employer's strategies;
• ignores commissions, slippage and the price impact of their own trades.

How long does it take to hire a Quantitative Researcher?

We present the first candidates for the Quantitative Researcher role within 5–7 days of the brief.

Related roles

• Data Scientist — applies similar methods to product and business data.
• ML Engineer — takes models to production but does not look for trading signals.
• Blockchain Engineer — builds the infrastructure and protocols crypto trading teams work with.
• Data Engineer — builds the market data pipelines researchers work on.