VP Risk & Quantitative Analysis

Franklin Templeton•Stamford, CT

1d•$147,000 - $160,000•Hybrid

About The Position

O’Shaughnessy Asset Management (OSAM) is part of Franklin Templeton, a forward-thinking asset manager that has built its success through powerful partnerships. We leverage cutting-edge strategies and deep insights to unlock opportunities for long-term wealth creation. Our talented, global teams bring expertise that is both broad and unique. O’Shaughnessy Asset Management is a research and money management firm based in Stamford, Connecticut operating autonomously and backed with global, enterprise resources. Their approach to managing money is transparent, logical, and completely disciplined, leading to long‐standing relationships with clients. OSAM is a leading provider of Custom Indexing services via its Canvas® platform which offers financial advisors an unprecedented level of control and ease in creating and managing personalized separately managed accounts (SMAs) that target improved after-tax outcomes. OSAM 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. Canvas is seeking a VP Risk & Quantitative Analysis to join the Investment Risk & Quantitative Analysis team within the broader Risk organization. The Risk team is responsible for identifying, assessing, and mitigating business, operational, and investment risks across the firm. Anchored in the firm's philosophy of Learn, Build, Share, Repeat, the team continuously evolves its frameworks and processes to enhance risk visibility and support informed decision-making. This role is focused on advancing the firm's quantitative capabilities across portfolio construction, optimization validation, and tax-aware investing. This role sits at the intersection of portfolio construction, risk analytics, and quantitative research. The position offers significant exposure to large-scale portfolio implementation across thousands of accounts, with a focus on improving tracking accuracy, tax efficiency, and overall portfolio outcomes. This is a highly visible opportunity to directly influence the evolution of Canvas's quantitative investment platform.

Requirements

5+ years of experience in quantitative research, portfolio construction, or a related investment role within investment management
Strong background in portfolio optimization, factor models, and direct indexing strategies
Strong technical and analytical expertise, with experience in portfolio optimization, direct indexing, and quantitative investment strategies
Experience evaluating or building tax-aware investment strategies, including tax-loss harvesting methodologies
Proficiency in programming and data analysis, including Python (and/or C#) and SQL
Familiarity with industry risk and analytics platforms such as Barra and Aladdin
Strong quantitative and problem-solving skills, with the ability to translate complex analyses into actionable insights
Experience working with large-scale portfolio datasets and account-level analysis
Strong communication skills, with the ability to partner effectively across investment, research, and risk teams
Ability to work independently in a fast-paced, collaborative environment and manage multiple priorities

Responsibilities

Enhance model transparency and robustness by independently validating optimization outputs, improving tax-alpha methodologies, and developing advanced risk and analytics frameworks
Partner closely with Portfolio Management, Research teams to evaluate model performance, diagnose portfolio outcomes, and enhance the firm's optimization and tax-aware investment processes
Create portfolio optimization(s) to independently validate optimization outputs, with a focus on identifying and analyzing discrepancies in tracking error and tax-loss harvesting results compared to our core portfolio optimizers at the account level
Evaluate and improve the firm's Tax Alpha model, assessing the effectiveness of tax-loss harvesting strategies and analyzing dispersion across portfolios and accounts
Design and implement advanced risk and performance diagnostics to better understand portfolio outcomes, including tracking error, factor exposures, and tax impacts
Lead the development of integrated risk checks leveraging Aladdin and/or Barra, and direct indexing data to analyze dispersion, identify underlying drivers, and provide actionable insights
Partner with Portfolio Management and Research teams to share findings and iterate framework and models based on feedback
Analyze portfolio performance drivers, including return, volatility, and tax impacts
Develop and maintain scalable analytics and tooling using Python (or C#), SQL, and other technologies to support ongoing research and monitoring
Contribute to the evolution of quantitative investment processes, including optimization techniques, tax-aware strategies, and portfolio construction frameworks