Data Scientist

Experience Required: 10+ years

You will be part of a high-profile Applied R&D team enabling creative and impactful solutions across Active Equities, Fixed Income, Risk Management, Corporate Finance, and more. Our diverse research lab leads the application of deep learning, convex optimisation, game theory, stochastic simulation, and more techniques for production solutions across all parts of Investment management. This opportunity is best aligned to individuals with experience in various parts of systematically designed research and engineering efforts. We are specifically looking for individuals with mathematical optimization and applied mathematics skill sets to complement the existing staff with this focus.

Must Have:

- Strong hands-on experience with convex optimization, mixed-integer optimization, and other optimization methods used in research settings.
- Solid background in quantitative modeling, problem formulation, and translating theory into practical solutions.
- Proficient in Python for research and development work.
- Ability to build, test, and operationalize models or optimization solutions in a computational environment.
- Experience with out-of-sample testing, simulation, and back-testing.
- Exposure to portfolio/risk/investment data and quantitative workflows is strongly preferred.
- Familiarity with Markowitz, Modern Portfolio Theory, Black-Litterman, and factor models.