Quantitative Research Analyst

About Franklintempleton

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. For more firm information, please visit www.osam.com

Job Summary

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.

Key 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.

Requirements

• Strong coding proficiency in C#, Python, and SQL.
• Deep familiarity with data structures and algorithms.
• Experience with quantitative modeling, optimization, and feature engineering.