Applied Research Data Scientist – Mathematical Optimization (Investment Management)

Introduction

A leading financial services organization is seeking an Applied Research Data Scientist specializing in Mathematical Optimization to join its collaborative research and investment technology team.

Day-to-Day Responsibilities

• Design, develop, and implement advanced mathematical optimization models to solve complex portfolio construction and investment management problems.
• Conduct applied research utilizing optimization methods, stochastic simulation techniques, and statistical modeling approaches.
• Build scalable prototypes and production-ready solutions using Python and cloud-based research platforms such as SageMaker and Databricks.
• Evaluate and validate models through rigorous testing methodologies including backtesting, simulation, and out-of-sample analysis.
• Collaborate closely with quantitative researchers, portfolio managers, and engineering teams to translate business objectives into analytical solutions.
• Analyze large financial and investment datasets to identify trends, generate insights, and enhance investment strategies.
• Interpret and implement methodologies derived from academic research papers and industry publications.
• Document research findings and communicate technical concepts effectively to both technical and non-technical stakeholders.
• Stay current on emerging developments in optimization, machine learning, quantitative finance, and applied research methodologies.

Required Skills & Qualifications

• Must-have qualifications:
• 5+ years of experience in applied research, mathematical optimization, quantitative modeling, or related analytical disciplines.
• Master's degree or PhD in Applied Mathematics, Operations Research, Computer Science, Engineering, Statistics, Physics, or a related quantitative field.
• Strong expertise in optimization methodologies including convex, mixed-integer, linear, and nonlinear optimization.
• Advanced Python programming skills with experience developing research models and production-ready analytical solutions.
• Experience working within research and data science environments such as AWS SageMaker, Databricks, or similar platforms.
• Expertise in model evaluation techniques including backtesting, simulation, validation, and out-of-sample testing.
• Proven ability to translate academic research into practical business applications and scalable solutions.
• Strong quantitative reasoning, problem-solving, and analytical skills.
• Experience working with large datasets and complex mathematical models.
• Ability to work in a hybrid environment in Malvern, PA (3 days onsite weekly).
• Ability to successfully complete all required pre-employment screenings, including background investigation, fingerprinting, drug testing, and employment verification.

Preferred Skills & Qualifications

• Nice-to-have skills:
• Experience developing machine learning models and architectures for quantitative applications.
• Knowledge of portfolio optimization, risk modeling, factor models, and other quantitative finance concepts.
• Experience supporting investment management, asset management, Active Equities, or Fixed Income research initiatives.
• Progress toward or completion of the CFA designation.
• Experience deploying research-based models into production environments.
• Hands-on experience with optimization frameworks and solvers such as Pyomo, Gurobi, CPLEX, or similar technologies.
• Proficiency with SQL, cloud technologies (AWS), and modern machine learning frameworks.
• Experience working in financial services, investment management, hedge funds, asset management, or quantitative research environments.