Senior Product Manager (Data Foundations) (Hiring Immediately)

Every day, somewhere in the world, important decisions are made. Whether it is a private equity company deciding to invest millions into a business or a large corporation implementing a new strategic direction, these decisions impact employees, customers, and other stakeholders.

Consulting and private equity firms come to proSapient when they need to discover knowledge to help them make great decisions and succeed in their goals. It is our mission to support them in their discovery of knowledge.

We help our clients find industry experts who can provide their knowledge via interview or survey: we curate this knowledge in a market-leading software platform; and we help clients surface knowledge they already have through expansive knowledge management.

Data is at the heart of what we do — and we're looking for the person who'll take it to the next level. We're hiring a Senior Product Manager to own our data foundations domain end-to-end. That means defining how data flows into our platform, how it gets structured and enriched, and how it ultimately helps our users make faster, smarter decisions. From knowledge graphs and AI-powered pipelines to third-party data partnerships, you'll shape the infrastructure and strategy that underpins the entire product.

This is a high-impact role for someone who's comfortable at the crossroads of product, data, and AI, someone who can translate complex technical trade-offs into clear product decisions, and who thrives on building things that scale.

Key duties in this role will include:

• Owning our data foundations product domain end-to-end: setting vision, defining strategy, and delivering tangible impact to deepen the breadth and quality of our data.

• Championing agile delivery with a focus on measurable outcomes and a strong commitment to solving real customer problems.

• Defining clear objectives and key results to align team efforts with the broader company strategy.

• Owning the end-to-end product lifecycle, from deep customer research to defining and delivering data-driven solutions.

• Leading the design and development of scalable data extraction and enrichment pipelines, working within a cross-functional squad and collaborating closely with data science, commercial teams, and end customers.

• Owning the knowledge graph product strategy end-to-end — working with engineering to define entity and relationship models, govern taxonomy decisions, and drive the roadmap from MVP through to full production rollout. This includes shaping retrieval architecture across structured and semantic search modalities, in close collaboration with platform engineering and external technology partners.

• Managing strategic technology partnerships — acting as the product counterpart to platform engineering when working with internal engineering teams or external implementation partners and cloud platform partners (e.g., Google Cloud / GCP). This includes defining project scope and success criteria, co-owning evaluation methodology (golden datasets, parameter sweeps, LLM eval), and translating technical trade-offs into product decisions.

• Identifying, evaluating, and integrating third-party data providers that enhance our platform's depth (e.g., firmographics, financials, technographics).

• Working closely with legal and compliance teams to ensure all data usage meets regulatory and privacy standards.

• Ensuring enriched data is accurate, structured, and discoverable across the internal platform and user-facing products.

Requirements

Key previous experience needed for this role includes:

• 5+ years of product management experience in data products, marketplace models, or AI-powered workflows.

• Experience conducting structured process discovery with operational or domain experts and translating it into formal data models or product requirements.

• Track record of delivering data products end-to-end, working closely with engineering teams through all stages of development and launch.

• Proven experience with knowledge graph concepts / GraphRAG — including property graph schemas, entity modelling, and hybrid retrieval approaches that combine structured graph traversal with semantic search. Hands-on exposure to the trade-offs between schema-governed and LLM-driven taxonomy design is preferred.

• Experience owning a domain ontology or entity taxonomy end-to-end — including defining node and edge types, managing schema evolution without breaking live queries, resolving modelling conflicts (e.g., when an entity fits multiple types), and enforcing consistency across engineering and data science contributors.

• Solid understanding of data pipelines, APIs, and working with unstructured]]>