The Skills Employers Want in Today's Quant Researchers

With insights from Selby Jennings’ Quantitative Analytics, Research & Trading team.

The definition of a strong quant researcher has changed, and the old shorthand of a mathematics or physics PhD with strong Python skills is no longer enough for many roles in 2026.

Today’s strongest candidates combine deep statistical rigor with production-grade programming and applied machine learning. That broader profile is harder to find as quantitative teams compete with tech companies, AI labs and established financial firms. In our mid-year update, Alex Morris, Senior Vice President of Selby Jennings, noted that, in many cases, 

 

Key takeaways

• Strong quant researchers pair maths, statistics and programming with practical machine learning experience.
• Employers want clear evidence of how a candidate has tested, challenged and applied their work.
• Production ready coding and reliable research processes carry real weight in the hiring process.
• Clear communication helps candidates show the commercial value of their research.

 

The core technical foundation: math, stats and programming

Despite the attention on AI, the fundamentals haven’t gone anywhere. A quant researcher still needs enough probability and statistical depth to distinguish a useful signal from noise.

That means being comfortable with hypothesis testing, statistical inference, time series analysis, regression and Bayesian methods, alongside concepts like overfitting, nonstationarity and multiple testing. Knowing how to run a test is only part of the skill. Researchers must also understand its assumptions, limitations and relevance to a trading decision.

Another non-negotiable is programming. Python remains the main research language, allowing quants to move quickly between data work, statistical analysis, modelling and backtesting. For trading-adjacent roles, employers may also expect C++ (and increasingly Rust) when performance, latency and systems-level control matter. This means that, for candidates, the ability to research a model is no longer enough. Employers increasingly want quants who can also turn that research into robust, production-ready code – particularly using Python, C++ or Rust.

 

Why machine learning is now table stakes

Machine learning has moved from an interesting extra to a core part of the quant toolkit. Demand for AI and ML talent surged in 2025, with applied ML experience increasing in value across signal generation, alpha research, execution and risk modelling.

For candidates, a CV or interview should show how that experience has been used. Be prepared to talk through:

• the research or trading problem you were trying to solve
• the data you selected and the features you built
• how you tested the model and managed overfitting
• what the results showed, including limitations
• what was required to take the work beyond a backtest

Alex Morris sees this distinction in the market: 

The strongest profiles combine statistical discipline, practical machine learning experience and the ability to explain where a model adds value.

 

The rise of engineering-grade coding ability

Teams are under pressure to turn research into usable systems, so the quality of a researcher’s code now carries far more weight than it once did. A promising backtest can quickly lose credibility when nobody can reproduce it, trace a change in the data or understand the assumptions behind the model.

That is why employers look for researchers who can build a process that others can trust and reuse. Clean data ingestion, reproducible feature engineering, realistic train-test splits, version control and robust backtesting all make it easier for a team to challenge an idea, improve it and take it forward.

In our experience, the strongest examples candidates can use tend to be projects where they can explain the full journey. This includes the original hypothesis, how they built and tested it, what changed as the work developed and how they dealt with the gap between research and implementation. This is especially valuable in execution, market microstructure and monetization roles, where research quality and practical delivery sit closely together.

 

Soft skills that separate good hires from strong hires

Quant researchers spend much of their time working through uncertainty, so clear communication and sound judgement matter alongside technical ability. A portfolio manager may care about whether a signal can improve returns. A trader may want to understand how it behaves in different market conditions. A developer may need clarity on how the model should run in practice.

Strong researchers can adjust the conversation without losing the substance. They can explain what they found, why it matters, how confident they are in the result and where the risks sit. They can also defend a view while remaining open to evidence that challenges it.
Resilience shows up in the same way. Research is iterative, and a strategy can fail or a promising backtest can disappear once testing becomes more rigorous. Candidates who can talk openly about those moments, what they learned and how they changed their approach will give employers a much stronger sense of how they work in a real research environment.
What this means for employers building quant teams

For employers, the quant researcher specification is becoming broader at exactly the moment the talent pool is more contested. There is growing competition from AI companies such as OpenAI and Anthropic, alongside demand across systematic trading, applied AI and execution focused roles.

This makes precision in hiring more important. Teams need to distinguish between candidates who can discuss machine learning and those who have used it to solve research problems. They also need to tell the difference between programmers who can prototype and engineers who can productionize, and between researchers who come up with ideas and those who can communicate and defend them.

For candidates, the message is clear. The market is asking for breadth and depth. The strongest quant research profiles combine rigorous statistical thinking, excellent coding, practical machine learning experience and the judgement to turn uncertain research into clear, testable decisions.
For a wider view of the quantitative hiring market, including talent competition, non compete trends and the areas where demand is growing, read our Quantitative Analytics, Research & Trading: Mid Year 2026 Talent Insights.

 

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