Implement, deploy, and maintain quantitative models for portfolio construction, risk, factor analytics, and systematic strategies in production.
Design and maintain scalable Python infrastructure for backtesting, optimisation, and live analytics, with strong testing and CI/CD practices.
Translate research methodology into production systems – portfolio optimisation, factor risk models, covariance estimation – using libraries such as CVXPY or Mosek.
Work with portfolio managers, researchers, and IT to translate research designs into deployable production‑quality code.
Maintain and improve the team's analytical infrastructure and engineering practices; stay current with relevant developments.
Qualifications
Master's or PhD in a quantitative field (Computer Science, Mathematics, Statistics, Engineering, Physics, Finance, or related).