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Senior Quantitative ML Researcher
James Okafor
Full-time · Senior · London
About the role
We are a systematic trading firm. We don't publish research. We don't conference. We don't have a blog. We have a portfolio of strategies that generate consistent risk-adjusted returns, and we intend to keep it that way. What we are looking for is a researcher with a genuine command of both statistical modelling and machine learning — someone who can distinguish between a signal and noise in a dataset that has been looked at by very smart people for a long time and found wanting. If you think that sounds hard, you're right. If you think you can contribute to it, we'd like to understand why. The role involves designing, implementing, and evaluating predictive models for financial time series. You will have access to large, clean, proprietary datasets and significant compute. You will not have access to a large team, a slow review cycle, or the kind of feedback loop that requires six meetings to produce a decision. Compensation reflects the difficulty of what we do and the value of doing it well. We will not publish a number here because it depends substantially on what you bring. For context: our total compensation packages for researchers at this level are in the top percentile of the industry.
Responsibilities
- Design and evaluate predictive models for price and volatility signals across multiple asset classes
- Conduct rigorous out-of-sample testing and document the assumptions and limitations of every model you propose
- Work independently on research projects with bi-weekly reviews — you manage your own pace and direction
- Contribute to shared research infrastructure: data pipelines, backtesting frameworks, and evaluation tooling
- Critique the research of other team members and expect the same rigour applied to your own
Requirements
- PhD or equivalent research experience in statistics, mathematics, computer science, or a quantitative field
- Strong Python for research and production: clean, tested, reproducible code
- R for statistical modelling, hypothesis testing, and data exploration alongside Python
- Deep statistics: time series analysis, factor modelling, Bayesian methods, and the discipline to question your own results
- PyTorch for designing and training custom model architectures — you write the training loop, you don't just call fit()
- SQL for working with structured financial and market data at scale
- A track record of producing results that held up out of sample — this is what we will ask about most
Benefits
- Total compensation in the top percentile of the quantitative finance industry — discussed individually
- Full remote for the research role with optional access to our London office
- Small team, minimal process, maximum compute
- No publication requirements, no conference obligations, no public profile
- 30 days annual leave
Job Type
Full-time
Level
Senior
Language
English
Salary Range
Competitive — top-of-market
AI Expertise
data science & analytics
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