Quantitative Research Scientist

Role- Quantitative Research Scientist – Portfolio Optimization

Malvern, Pennsylvania, Hybrid

Years of Experience

Mathematical optimization- Strong hands-on experience with convex optimization, mixed-integer optimization, and other optimization methods used in research settings.

Applied mathematics- Solid background in quantitative modeling, problem formulation, and translating theory into practical solutions.

Python development- Proficient in Python for research and development work.

Research-to-production-Ability to build, test, and operationalize models or optimization solutions in a computational environment.

Evaluation frameworks-Experience with out-of-sample testing, simulation, and back-testing.

Investment management domain-Exposure to portfolio/risk/investment data and quantitative workflows is strongly preferred.

Quant finance concepts-Familiarity with Markowitz, Modern Portfolio Theory, Black-Litterman, and factor models.

Reading research papers- Ability to read, reproduce, and adapt academic or industry research in code.

Responsibilities

• 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 optimization, 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.

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

· Ability to read & reproduce research papers in computational settings

· Participation in systematic or quantitative workflows in Investment Management is a plus