Product Manager, Alternative Data - Data Evaluations and Onboarding

Bloomberg•San Francisco, CA

4d•Onsite

About The Position

The Bloomberg Alternative Data team is a fast-growing, high-impact product group building the next generation of data and analytics for company research and intelligence. Our mission is to make alternative data as essential to investment workflows as traditional fundamentals - at global scale. As product managers, we sit at the intersection of data science, engineering, sales, and clients, and are responsible for some of Bloomberg's most differentiated data capabilities. A core part of that mission is ensuring we can identify, evaluate, and productize the best external data - quickly and at scale. Bloomberg's Alternative Data team is seeking a Product Manager to own our dataset evaluation and onboarding function - the critical process by which we determine whether a dataset meets our product strategy and turn contracted data into production-ready product capabilities. This is a highly cross-functional, strategic, and analytically rigorous role. You will build and manage the systems that allow Bloomberg to evaluate datasets efficiently, make sound investment decisions, and onboard new data sources at scale. You will work across the full data partnership lifecycle - from translating product requirements into evaluation frameworks, to managing the team’s evaluation pipeline, to getting newly contracted datasets production-ready. You will have a direct impact on the quality and pace of Bloomberg's alternative data product portfolio, with visibility across product leadership, engineering, data science, and our data vendor ecosystem.

Requirements

5+ years of product experience, with meaningful time spent on alternative data products.
Strong understanding of alternative data and how institutional investors use it.
Demonstrated experience building or managing structured analytical or operational processes, ideally in a data evaluation, data quality, or data onboarding context.
Hands-on experience working with data - including data quality frameworks, QA practices, and the operational realities of delivering reliable data at scale.
Demonstrated ability to translate high-level product requirements into detailed operational and technical specifications.
Experience working directly with data engineers, data scientists, and cross-functional stakeholders in large organizations.
Experience working directly with third-party data vendors or managing external data provider relationships.
Strong written and verbal communication skills; able to produce clear, decision-ready outputs for product leadership and external partners alike.
Exceptional organizational skills and the ability to manage a concurrent pipeline of evaluations and onboarding workstreams without losing sight of timelines or quality.

Nice To Haves

Product management experience building data, analytics, or research platforms.
Experience operating in large organizations with multiple stakeholders.

Responsibilities

Own the Dataset Evaluation Process: Manage the evaluation pipeline end-to-end: prioritization, timelines, and throughput across a concurrent set of active evaluations.
Translate product strategy requirements into structured, rigorous evaluation frameworks: defining the criteria, methodology, and outputs by which datasets are assessed against the product bar.
Collaborate with the engineering evaluations team: setting requirements for their analytical work and ensuring results are transparent and decision-ready for product leadership.
Engage directly with data providers during evaluations: efficiently resolving questions and gather the information needed to complete assessments.
Continuously improve evaluation processes over time: standardizing, streamlining, and productizing evaluation workflows to increase speed, rigor, and scalability.
Drive Data Onboarding & Productization: Own the end-to-end onboarding plan for newly contracted datasets - from establishing delivery and technical requirements with the data provider to managing dependencies, launch criteria, and timelines.
Translate product and business requirements into precise, implementation-ready data and operational specifications - bridging the gap between what the product needs and what engineering and providers must deliver.
Define and manage ongoing data quality monitoring for onboarded datasets, ensuring production reliability post-launch.
Drive Cross-Functional Execution: collaborating with data scientists, engineers, data scouts, other product managers and data vendors across the partnership lifecycle.
Define and Own Success Metrics: Establish metrics for evaluation throughput, onboarding speed, and data quality - and track them rigorously.
Continuously assess and improve the efficiency and scalability of both the evaluation and onboarding processes.