Quantitative Researcher (X-asset, mid-freq)

The work

This is a systematic quant researcher role within a cross-asset trading environment spanning holding periods from hours to weeks. You will own the full research pipeline: signal generation, testing, portfolio-level analysis, and work with live capital.

The asset universe is broad: equities, futures, FX, credit, commodities, and ETF structures all feature. The problems are genuinely hard: signal decay, regime sensitivity, execution friction, and cross-asset correlation structure all matter here. You will be expected to form views, test them rigorously, and defend them.

The infrastructure

You will have access to data and compute infrastructure at a scale very few firms can match. Research here is not constrained by tooling. It is constrained by the quality of your ideas.

How the team operates

Research here is a shared endeavour, not a collection of siloed books. Every researcher has full visibility into every active strategy's code. There are no black boxes, no protected territories. The expectation is that collective understanding produces better research than individual ownership.

Strategies are sized for their contribution to the portfolio as a whole, not as standalone entities. That means your work is evaluated at the system level, which rewards researchers who think carefully about covariance, capacity, and cross-strategy interaction, not just isolated backtest Sharpe.

What we're looking for

You have 3-6 years of experience in a systematic trading environment, a hedge fund, prop trading firm, or closely related research role. You have built and shipped predictive models against real market data, not just in simulation.

Core requirements:

• Strong statistical foundations: time-series analysis, factor modelling, signal research
• Python proficiency; C++ experience strongly preferred (you will be interacting with C++ day to day)
• Experience across more than one asset class, or a clear track record in one with genuine appetite to work cross-asset
• Ability to take a research idea from hypothesis to backtested strategy to production-ready code
• Comfort operating in an environment where your work is visible and subject to peer scrutiny

The right mindset:

You are intellectually honest about what your models do and don't explain. You are more interested in understanding market structure than in protecting alpha. You find the idea of a shared codebase appealing rather than threatening.

Where?

London is a focus area, but realistically anywhere across the major financial hubs (NYC, Singapore, Hong Kong, Chicago).