Data Scientist

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. Qualifications: · Significant experience in research settings regarding mathematical optimization and other modeling paradigms · Experience building machine learning architectures to address specific problem statements is nice to have * Comfortable with Quant standards such as Markowitz and Modern Portfolio Theory, mixed-integer optimization, Black-Litterman, factor models, etc. · Proficient with Python in development environments such as SageMaker, Databricks, etc * Experience creating evaluation frameworks with OOS, Sim, and back-test components and analyzing results · Experience with Investment Management related data and tasks is preferred · Participation or completion of the CFA or related financial knowledge is valuable · Participation in systematic or quantitative workflows in Investment Management is a plus