Quant Research
Quantitative Research Recruitment
Search for researchers whose work reaches live risk: signal generation, statistical modelling, machine learning and portfolio construction inside hedge funds, proprietary trading firms and market makers.
Quantitative research hiring rarely fails on technical ability. It fails on fit between what a researcher has genuinely owned and what a desk needs them to own from day one.
Our qualification is built around that distinction: which signals a candidate constructed rather than maintained, how their work reached production, what capacity it carried and how performance was attributed.
01
How we qualify research ownership
A CV rarely distinguishes between the researcher who designed a signal family and the researcher who inherited it. We ask for the specifics before an introduction is made.
- 01Signals or strategies personally constructed, and the hypothesis behind them
- 02Statistical modelling methods used, and why they were chosen over the alternatives
- 03Machine learning applied to real research problems rather than to coursework
- 04Alternative data evaluated, licensed or discarded, and on what evidence
- 05Portfolio construction, sizing and risk decisions the candidate influenced
- 06Capacity carried by the research and how it degraded over time
02
Research that reaches production
Research-to-production is a common failure point in a quant career and a common gap in a hiring brief. Some firms expect researchers to ship; others separate research and implementation entirely.
We establish which model a candidate has worked in, how close they have sat to live strategy exposure, and whether they have been accountable for a strategy after it went live rather than only before.
03
Commercial impact
Attribution matters more than headline performance. We look for a clear account of what a researcher contributed to a book, how that contribution was measured internally and how it survived regime changes.
Where compensation is discussed, it is discussed on the basis of what the market is actually paying for comparable ownership, not on aspiration.
04
Levels we cover
- 01Quantitative Researchers, early career through to senior
- 02Systematic Researchers across equities, futures, macro and credit
- 03Machine Learning Researchers working on production research problems
- 04Portfolio Managers running systematic books
- 05Heads of Research and team leads building research functions
Common technical ground
- Python
- Statistics
- PyTorch
- Alternative Data
- Portfolio Construction
- Backtesting
- kdb+/q