Hiring nowInvestment, quantitative finance and market AI

Quantitative ML Researcher

Also called Quant ML Researcher

A Quantitative ML Researcher tests investment ideas with models and builds evidence that holds up after costs and constraints. They stop the company trading on backtests that look good but fail in live conditions.

What they do

They turn an investment hypothesis into a testable study. They clean and align market data, build features, and set up train, validation and holdout splits that stop future information leaking into the past. They fit time-series and machine learning models, then run backtests that include trading costs, capacity limits and portfolio constraints. They use optimisation to turn signals into positions. They write up the study, record what was tried and what failed, and hand it to an independent reviewer who tries to break it. They then revise or drop the idea based on what that challenge finds.

What a strong hire delivers

In the first months, a strong hire delivers a documented research study on a live hypothesis, with a backtest that someone independent has challenged and signed off. They set up a repeatable method for leakage checks and holdouts. They give the team a clear view of which ideas survive costs and constraints, and which should be dropped early.

How you can tell they are good

Ask them to walk through a past study from hypothesis to result. Listen for how they controlled leakage, chose holdouts and counted the number of ideas tried. Ask what they dropped and why. Strong candidates describe failures plainly and welcome challenge. Be wary of anyone who quotes headline backtest results without discussing costs, turnover or how the test could be wrong.

Why it is hard to fill

The title is used loosely, so a CV can mean anything from pure statistics to execution engineering. Many people have strong machine learning skills but little feel for market data, costs and overfitting. Others know finance well but have limited modelling range. The overlap is small, and most of those people are already settled in research teams. Judging quality takes someone who has seen backtests fail.

Where to find them

Look at quantitative researchers and analysts in asset managers, hedge funds and trading firms. Also look at statisticians, econometricians and operations research specialists who have worked with financial time series. Model validation teams in banks produce people who are used to challenge and documentation. Some come from trading systems teams or academic research in finance, statistics or applied machine learning.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Quantitative ML Researcher

  1. Describe a study where a promising backtest failed. What did you find, and what did you change afterwards?

    A strong answer names the specific cause, such as leakage, costs or regime dependence, and the change in method that followed. Worry if they cannot recall a failure or blame bad luck without evidence.

  2. How do you design train, validation and holdout splits for financial time series?

    Look for time-ordered splits, purging or embargo around overlapping labels, and a holdout touched once. Good answers mention how repeated testing contaminates holdouts. Worry about random shuffling or reusing the holdout freely.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for a Quantitative ML Researcher to test investment ideas with rigorous, leakage-controlled research. You will work with market data, models and portfolio constraints, and your studies will be challenged independently before they inform any decision.

What you will do

  • Turn investment hypotheses into clear, testable research studies
  • Build and validate datasets and features from market data
  • Design splits and holdouts that prevent information leakage
  • Run backtests that include costs, constraints and capacity limits
  • Use optimisation to construct portfolios from model signals
  • Document results, including failed ideas, for independent review
  • Revise or retire ideas based on challenge and new evidence

What you will bring

  • Demonstrated research on financial or other noisy time-series data
  • Strong grounding in probability, statistics and time-series modelling
  • Practical experience building backtests and checking them for bias
  • Experience with optimisation methods in a portfolio or similar setting
  • Clear written explanations of method, results and limitations
  • Comfort being challenged and reproducing your own work

Nice to have

  • Experience in financial model validation
  • Familiarity with execution or transaction cost modelling
  • Work with research and backtesting platforms at scale

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Financial model validationAdvancedChallenge financial-model assumptions and limitations
  • Time-series modellingAdvancedModel temporal dependencies and forecasts
  • Probability and statisticsWorkingQuantify variability and statistical evidence
  • Optimisation and operations researchWorkingChoose actions subject to objectives and constraints

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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