Job Description
This is a great opportunity to join a systematic trading team in a global Hedge Fund based in London. To succeed in this role you must have advanced coding (Python) skills.
You'll need to be able to work at pace produce quality work with strong attention to detail. Previous experience of operating in a financial markets trading environment would be extremely useful.
It's highly likely that you'll have a masters degree or PhD (e.g. computational finance or a STEM subject) and a very clearly evident drive to learn and succeed with a clear passion for the financial markets.
**A 6/12 month internship opportunity to commence February / March 2026**
We are looking for:
* Probability & statistics : distributions, estimation, hypothesis testing, multiple testing, overfitting/selection bias, basic Bayesian thinking.
* Time series & econometrics : stationarity, autocorrelation, regime shifts, volatility modelling basics, forecasting evaluation.
* Optimization & linear algebra : convex optimization intuition, matrix methods, numerical stability.
* Stochastic processes (helpful)
* Practical engineering skills: Git, unit tests, packaging, logging, debugging, performance profiling, and writing code other people can maintain.
* Ability to work with messy, high-volume datasets (corporate actions, roll adjustments, survivorship bias, timestamp issues).
* Understanding of market microstructure data if relevant (quotes/trades, order book, slippage, latency).
* Building reproducible pipelines (clear dataset definitions, versioning, auditability).
* Designing experiments and avoiding common traps: lookahead bias, leakage, data snooping.
* Robust validation: walk-forward testing, cross-validation appropriate to time series, out-of-sample hygiene.
* Sensitivity analysis: parameter stability, regime dependence, feature importance, transaction cost realism.
* Clear communication of results: not just Sharpe, but why it should persist and when it fails .
You should also understand:
* Instrument basics (equities, futures, FX, rates, options).
* PnL drivers, exposures, risk factors, and constraints (leverage, margin, liquidity).
* Transaction costs and real-world frictions: spreads, market impact, borrow/financing, roll, funding.