Quantitative Research Analyst enhancing portfolio optimization and tax-aware portfolio construction frameworks at OSAM. Collaborating with teams to translate research insights into production systems for investment strategies.
Responsibilities
conduct investment research: projects will involve running simulations of investment strategies to determine after-tax effects
run large-scale simulations and backtests to assess strategy efficacy and after-tax implications
interact with robust data pipelines and APIs using C#, Python, and SQL, ensuring data quality, reproducibility, and efficient computation
support integration of research outputs into production portfolio management systems used by PMs and traders
interface directly with portfolio managers, risk teams, and technology groups to communicate model results, limitations, and implementation details
apply statistical and machine learning methods (e.g., regression, optimization, NLP, feature engineering) to generate, validate, and enhance alpha factors
Requirements
Degree in Computer Science, Statistics, Math, Engineering, Physics, or Quantitative Finance
Master’s or Ph.D. preferred for deeper specialization in quantitative methods, data science, or optimization
2-5 years of relevant experience in quantitative research, financial data engineering, or software development for investment applications
Strong proficiency in C# (or Java), Python, and SQL for data analysis, modeling, and production code
Experience with object-oriented programming and modular system design in a collaborative environment (Visual Studio, Git)
Deep understanding of relational databases, schema design, and query optimization
Expertise in statistical modeling, time-series analysis, feature engineering, and machine learning applications for finance
Knowledge of portfolio optimization, risk modeling, and factor-based investing
Familiarity with convex optimization, quadratic programming, and constrained portfolio problems
Excellent communication skills — able to explain complex quantitative concepts to both technical and non-technical audiences
Highly organized, detail-oriented, and comfortable managing multiple concurrent research projects
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