Team Lead, Product Management – Quantitative Data Solutions New York, NY Posted today

Team Lead, Product Management – Quantitative Data Solutions

Location

New York

Business Area

Product

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Bloomberg is building a comprehensive suite of normalized, linked and point-in-time datasets for quantitative, systematic and quanta mental investment research. The portfolio brings together company fundamentals, estimates, pricing, supply-chain relationships, industry and segment-level data, macroeconomic indicators, commodity supply and demand data, and alternative data through interoperable products designed for research and production workflows.

We are establishing a new Macro and Commodity Research Data vertical and are looking for an experienced Product Manager Team Lead to define its strategy, build its product portfolio and lead its development.

This role requires a strong understanding of macroeconomic and commodity markets, the data used to analyse them, and the workflows through which investment managers turn data into signals, forecasts, portfolio decisions and risk views. The successful candidate will also understand how AI, agentic research tools and modern data infrastructure are changing the way clients discover, evaluate and consume financial data.

You will be responsible for shaping a differentiated portfolio spanning areas such as economic releases and surveys, government auctions, commodity supply, demand and inventories, physical flows, positioning, weather, outages and other market-relevant datasets. You will determine where Bloomberg can create distinctive client value, how the products should work together, and how the business can convert that value into sustainable commercial growth.

The Research Data business is an important part of Bloomberg Enterprise Data’s growth strategy. Our objective is to solve complex research and data-management problems for quantitative, systematic and fundamental investment teams, while making Bloomberg data easier to discover, evaluate, integrate and use across client workflows.

• Define the strategy, positioning and multi-year roadmap for the Macro and Commodity Research Data vertical, translating market developments and client needs into clear product priorities.

• Lead and develop a team of product managers and subject-matter experts, establishing clear responsibilities, decision processes, objectives and measures of success.

• Build deep domain expertise across systematic macro, commodities and multi-asset research, including how clients combine economic, physical-market, pricing, positioning and alternative datasets to generate signals and manage risk.

• Develop a strong understanding of the commercial opportunity for the vertical, including addressable markets, client segments, competitive positioning, packaging, pricing and monetisation models.

• Own the business case for product investment by assessing client value, revenue potential, development cost, strategic differentiation and opportunity cost.

• Engage senior clients, researchers, portfolio managers, data scientists and data engineering teams to identify unmet needs, test product concepts and validate priorities.

• Translate client workflows into well-defined data products, including requirements for point-in-time integrity, historical depth, metadata, identifiers, lineage, accessibility, interoperability and production use.

• Set measurable product and commercial outcomes, monitor adoption and revenue performance, and adjust the roadmap based on evidence rather than activity alone.

• Manage product specification, prioritisation and delivery across data, engineering, sales, implementation, support and other Bloomberg teams.

• Ensure that individual products form a coherent portfolio, with common design standards and clear connections across macro, commodities, pricing, reference data and related Bloomberg content.

• Represent the vertical internally and externally, helping sales teams explain its value and building credibility with sophisticated quantitative and institutional clients.

• Stay current on developments in financial markets, systematic investment research, data science, AI-enabled workflows and the competitive data…