Principal Product Manager, Data Product & Data Science

Later•Boston, MA

1d•Hybrid

About The Position

Later’s biggest opportunity in the creator economy is turning our unique, multi-source creator data into meaningful intelligence that drives better outcomes for brands, creators, and our internal teams. As Principal Product Manager, Data Product & Data Science, you will own the strategic data product roadmap that powers how creators are understood, evaluated, and matched to the right brand opportunities. This is a net-new, highly visible role reporting directly to the Chief Product Officer. You will operate at the intersection of product strategy, data engineering, data science, and go-to-market teams—shaping how raw data becomes trusted signals, differentiated insights, and defensible competitive advantage across Later’s platform, agency, and services offerings.

Requirements

8+ years of product management experience, with meaningful ownership of data-heavy or platform-level products.
Demonstrated experience building or scaling data products, not just dashboards—schemas, pipelines, signals, or models that power downstream use cases.
Hands-on comfort with data tools and concepts (e.g., SQL, BigQuery, analytics workflows) and the ability to engage deeply in the details.
Experience working alongside data science teams on algorithms, modeling, or applied ML—even if you were not the primary model builder.
A proven ability to zoom out to the strategic narrative and zoom in to the weeds when necessary.
Strong stakeholder management skills and a track record of influencing senior leaders in ambiguous environments.

Nice To Haves

Experience at a data-first or analytics-driven company.
Exposure to social platforms, creator economy products, advertising technology, or agency environments.
Familiarity with programmatic advertising, marketplace dynamics, or matching/recommendation systems.

Responsibilities

Own and evolve the end-to-end creator data product strategy, spanning data acquisition, enrichment, modeling, quality, and insight generation.
Define and maintain a long-term roadmap that improves the breadth, depth, freshness, and reliability of creator and audience data across all internal and external sources.
Identify high-leverage data opportunities that unlock differentiation for Sales, Strategy, and Agency teams—turning data into a compelling narrative brands can buy into.
Translate ambiguous business problems into clear data product bets, success metrics, and sequencing decisions.
Partner closely with Data Engineering and Data Science to shape schemas, pipelines, models, and feature sets that support scalable data products.
Drive improvements to creator-level data including (but not limited to): social content signals, audience attributes, campaign performance metrics, Link in Bio behavior, commerce outcomes, and historical brand partnerships.
Define and track a Data Quality Score and related KPIs that quantify completeness, accuracy, timeliness, and usability.
Work hands-on with datasets using SQL and analytics tools to validate assumptions, explore opportunities, and pressure-test solutions.
Guide development of data-derived insights that improve brand–creator matching, campaign planning, and performance prediction.
Serve as a trusted product partner to Strategy, Sales, Agency/Services, Search, Campaigns, Reporting & Analytics, and Platform Services teams.
Align cross-functional stakeholders around shared definitions, priorities, and tradeoffs for data initiatives.
Act as the connective tissue between technical teams and business leaders—ensuring data products are both technically sound and commercially meaningful.
Influence without authority, using clarity, data, and strong product judgment to move teams forward.
Set a high bar for data product thinking, rigor, and storytelling across the Product organization.
Mentor other product managers on data literacy, experimentation, and outcome-oriented product discovery.
Champion a culture of curiosity, accountability, and customer-driven decision-making.
Stay ahead of emerging trends in data products, applied ML, creator analytics, and measurement.
Continuously assess new data sources, modeling approaches, and tooling that could strengthen Later’s competitive position.
Bring external best practices into the organization while adapting them to Later’s scale and business model.