Your statistical work will directly shape the methodology of a company built by an Oxford professor and a hedge fund manager, now scaling fast after closing their Series B.
This is a fully remote role, so you work from wherever suits you. No commute, no office politics, just serious quantitative work with people who understand the craft.
You will spend your time building and improving forecasting models, working across time series, regression modelling and seasonality. You will be thinking carefully about how the company analyses data and finding ways to make the methodology sharper, more robust and more defensible. This is not a role where you inherit a finished system and maintain it. You are improving how things are done, and your fingerprints will be on the output.
The company sits at the intersection of alternative data and financial services, which means the problems are genuinely interesting and the stakes are real. Clients care deeply about accuracy, and you will be the kind of person who does too.
You are comfortable in Python and you bring solid experience across statistics and quantitative modelling. You have gone deep in at least one advanced area, whether that is Bayesian modelling, Kalman filtering, change-point detection or forecast combinations. You have either spent five or more years doing this kind of work professionally, or you have a completed PhD in a relevant field. Either path works here. You give this role your full attention, no side projects or part-time arrangements.
The company is at a stage where your decisions matter, the team is growing, and the methodology is still being shaped. That is a rare thing.
Ready to do the most interesting quantitative work of your career?
Get in touch.
No up-to-date CV required.