Quantitative Research Analyst

O’Shaughnessy Asset Management (OSAM) is owned by Franklin Templeton, a dynamic firm that spans asset management, wealth management, and fintech, giving us many ways to help investors make progress toward their goals. With clients in over 150 countries and offices on six continents, you’ll get exposed to different cultures, people, and business development happening around the world.

O’Shaughnessy Asset Management (OSAM) operates independently as a Specialist Investment Manager and is a research and money management firm based in Stamford. Our approach to managing money is transparent, logical, and completely disciplined, leading to long‐standing relationships with our clients. We are a leading provider of Custom Indexing services via CANVAS®. CANVAS® is a platform offering financial advisors an unprecedented level of control and ease in creating and managing client portfolios in separately managed accounts (SMAs). Advisors can set up custom investment templates, access factor investing strategies, utilize passive strategies, actively manage taxes, and apply ESG investing and SRI screens according to the specific needs, preferences, and objectives of individual clients.

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Quantitative Research Analyst

OSAM is hiring a Quantitative Research Analyst to join our growing Research team. You will help enhance our portfolio optimization and tax-aware portfolio construction frameworks, supporting both simulation and live portfolio workflows. You will collaborate with portfolio managers, developers, and researchers to ensure research insights translate efficiently into production systems. Strong coding proficiency in C#, Python, and SQL, deep familiarity with data structures and algorithms, and experience with quantitative modeling, optimization, and feature engineering are essential.

Location : Stamford, CT or New York City, or other nearby location (East Coast time zone preferred) with a hybrid or remote work schedule.

What are the responsibilities of the Research Team?

The Research team is focused on optimization, risk, and tax research for the CANVAS® platform in addition to understanding what drives security returns. Our research is a scientific process using the combination of statistics and computer science applied to finance. The guiding philosophy of the firm is Learn, Build, Share, Repeat. The research team is dedicated to this evolutionary cycle.

How You Will Add Value

You will conduct investment research : projects will involve running simulations of investment strategies to determine after-tax effects. Projects may include identifying, measuring, and implementing potential improvements to any aspect of OSAM’s investment process.

You will run large-scale simulations and backtests to assess strategy efficacy and after-tax implications.

You will interact with robust data pipelines and APIs using C#, Python, and SQL, ensuring data quality, reproducibility, and efficient computation.

You will support integration of research outputs into production portfolio management systems used by PMs and traders.

You will interface directly with portfolio managers, risk teams, and technology groups to communicate model results, limitations, and implementation details.

You will apply statistical and machine learning methods (e.g., regression, optimization, NLP, feature engineering) to generate, validate, and enhance alpha factors.

What Will Help You Be Successful in This Role

Candidates should have demonstrated ability to generate ideas and see them through a research cycle, knowing how to prioritize and work collaboratively. Candidates should possess the following qualifications :

Education & Experience

Degree in Computer Science, Statistics, Math, Engineering, Physics, or Quantitative Finance.

Master’s or Ph.D. preferred for candidates with 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.

Programming & Data Skills

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.

Quantitative Methods

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.

Optimization

Familiarity with convex optimization, quadratic programming, and constrained portfolio problems.

Soft Skills

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.

Compensation :