Winton is a global investment management and data science company. Founded by CEO David Harding in 1997, Winton’s business is grounded in the belief that the scientific method can be profitably applied to the field of investing. The firm aims to help savers and institutional clients meet their investing goals by building intelligent, long-term investment systems that evolve as markets change. Winton employs more than 300 people in eight offices around the world and manages approximately $25 billion of assets for many of the world’s largest pension funds, sovereign wealth funds, banks and fund platforms.
As a quantitative researcher you will be responsible for developing automated quant trading strategies using sophisticated statistical techniques. The role will entail the leadership and management of statistical modelling projects as part of our research programme and, in time, responsibility for a significant theme of work within it. We are looking for individuals who are enthusiastic about taking a hands-on approach and collaborating with top technical talent in a collegial, meritocratic work environment characterised by teamwork and intellectual rigor.
- Statistical modelling of financial and non-financial datasets, examining real-world data.
- Delivering high quality statistical research output against our research goals.
- Create and test complex investment ideas and partner with our engineers to test your theories.
- Attending internal lectures/workshops and presenting your research for peer review.
- Collaboration with research colleagues.
- A world-class mathematical/statistical research education (Masters/PhD).
- Extensive experience in financial data analysis with a focus on a highly rigorous and technical approach to modelling.
- Experience of building and running systematic trading strategies.
- A background in statistical research for systematic investment management activities (returns forecasting, risk modelling, market impact modelling etc).
- Team leadership experience is desirable.
- Ability to tackle in-depth research projects and communicate complex ideas clearly.
- Demonstrate intermediate skills in at least one programming language.