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Franklin Templeton · 1 month ago

Quantitative Portfolio Manager - Custom Indexing (L/S strategies)

Stamford, Connecticut, United States of America

Full-time

Hybrid

Senior Level

$195K/yr - $225K/yr

5+ years exp

Franklin Templeton is a dynamic firm that spans asset management, wealth management, and fintech. They are seeking a Quantitative Portfolio Manager to develop, implement, and manage systematic long-only and long-short equity strategies, contributing to the firm's investment platform and aligning with client objectives.

Financial ServicesHedge FundsProfessional ServicesWealth Management

No H1B

Responsibilities

Develop and refine factor-based models targeting persistent sources of alpha

Conduct empirical research into new signals, portfolio construction methods, and cross-sectional and time-series relationships

Incorporate insights from behavioral finance, accounting changes, and market microstructure to improve alpha efficiency

Collaborate with Research Analysts and Quant Developers to evaluate factor performance, turnover, and risk

Design and manage long-only and long-short portfolios that balance alpha generation, liquidity, and risk constraints

Utilize optimization frameworks to manage exposures, sector constraints, and factor diversification

Monitor real-time risk exposures, attribution, and performance drivers across multiple investment universes

Integrate and enhance risk models (statistical and fundamental) to support portfolio and firm-level oversight

Implement systematic position sizing, short borrow management, and leverage controls consistent with mandate guidelines

Partner with the Trading teams to ensure efficient execution of trades with minimal slippage and market impact

Qualification

Quantitative portfolio managementPortfolio optimizationRisk managementPythonSQLC#Empirical researchCFA designationData-driven decision makingCollaboration

Required

5+ years of experience in quantitative portfolio management or research, with direct exposure to long-only, long-short equity and/or multi-factor strategies

Advanced degree (Master's or Ph.D.) in Finance, Mathematics, Statistics, Computer Science, Engineering, or related field

Strong programming skills (Python and SQL required; C# preferred) and familiarity with large data environments

Deep understanding of portfolio optimization, risk models, and execution cost modeling

Demonstrated ability to manage live portfolios and make data-driven investment decisions

Must be eligible to work in the U.S. without current or future sponsorship – unable to provide visa support