Job Title: Applied Research Data Scientist – Mathematical Optimization (Contract)
Location: Malvern, PA
Onsite Requirements: Hybrid – 3 days onsite (Tuesday–Thursday); candidates must be local to PA/NJ
Compensation Range: W2: $80–$95/hour | C2C: $80–$100/hour
Benefits:
Opportunity to work on high-impact investment and asset management initiatives.
Long-term 6+ month contract with strong extension potential.
Collaborative environment with quantitative researchers and engineering teams.
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. This role is ideal for a candidate who thrives at the intersection of advanced mathematics, quantitative research, machine learning, and software engineering. The successful candidate will leverage optimization techniques, statistical modeling, and large-scale data analysis to solve complex portfolio construction and investment management challenges.

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 that candidates must meet to be considered.
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 that would make a candidate more competitive but are not required.
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.