Client Portfolio Manager, Brooklyn Direct Indexing

TIAA•New York, NY

1d•Onsite

About The Position

The Brooklyn Investment team is building the next generation of direct indexing — a platform that fuses institutional-grade quantitative research, scalable technology, and hands-on client expertise. Our mission is to deliver hyper-personalized, tax-aware portfolios at scale. As a Client Portfolio Manager (CPM), you will sit at the intersection of clients, quantitative research, and technology, translating complex analytical insights into actionable client outcomes. You will partner closely with portfolio managers, engineers, and customer success representatives to customize portfolios, communicate performance and risk, and help scale a platform that is redefining wealth management. This is a high-visibility role requiring fluency in markets, proficiency in Python, and the credibility to engage effectively with sophisticated allocators and advisors.

Requirements

Minimum of 3+ years of direct indexing and python coding experience
Demonstrated expertise in equity markets, factor-based risk frameworks, and tax-aware investment strategies.
Practical proficiency in Python with the ability to independently manipulate data, conduct analyses, and collaborate effectively with quantitative researchers and software engineers.
Exceptional communication skills with the ability to move fluidly between detailed technical discussions and clear, client-ready narratives tailored to sophisticated audiences.
Proven track record of building and sustaining trust with institutional clients, financial advisors, or other high-caliber allocators.

Nice To Haves

CFA preferred.

Responsibilities

Serve as a primary ambassador of the direct indexing platform by engaging directly with clients and advisors, building trust and delivering a high-quality investment experience.
Interpret and communicate portfolio data — including exposures, tax trades, drift, and risk — in a clear and actionable manner for a sophisticated client audience.
Conduct quantitative analyses using Python, including pulling time series data, diagnosing factor exposures, validating tax trades, and building diagnostic dashboards.
Collaborate with research and engineering teams to translate client needs and feedback into platform features and workflow improvements.
Proactively monitor accounts to identify and act on opportunities related to tax-loss harvesting, portfolio transitions, customization enhancements, and compliance with client-specific investment restrictions.