Senior Product Manager, Data Platform

Manifold AI•Boston, MA

19h•Hybrid

About The Position

At Manifold, we are rebuilding how life sciences organizations work in the age of AI. Life sciences is a $2 trillion industry where productivity directly impacts patient outcomes. Yet despite major advances in biology, data generation, and AI, progress has slowed. Critical scientific questions are still blocked by fragmented data, brittle infrastructure, and workflows that were never designed for secure collaboration or AI-driven analysis. Manifold is the AI platform for life sciences, purpose-built to close this gap. We believe vertical AI is the next platform shift, and life sciences requires a domain-specific approach. Manifold combines agentic AI, deep life sciences context, and enterprise-grade governance to help teams move from scientific question to insight faster, so life-changing medicines reach patients sooner. Manifold is used by leading organizations across industry and academia, including Foundation Medicine, the Broad Institute, and the University of Virginia. The platform supports tens of thousands of users across hundreds of organizations globally. Manifold is backed by Reach Capital, TQ Ventures, Calibrate Ventures, SilverArc Capital, and Industry Ventures, with $40M raised to date. Our Culture At Manifold, we value intellectual rigor, humility, and mission-driven collaboration. We believe that technology is only as powerful as the people behind it, and we’re building a culture that supports growth, inclusion, and curiosity. We work fast, think deeply, and strive to make a lasting impact on patients’ lives. About the Role Manifold is hiring a Senior Product Manager to own the product strategy and execution for our data platform: the infrastructure layer that determines whether researchers can move from scientific question to insight at the speed their work demands. Every delay in drug discovery is measured in patient lives. Today, researchers still spend weeks wrangling fragmented datasets, waiting for pipelines, and manually tracking provenance across disconnected systems. The insights are there. The ambition is there. The AI capabilities are there. But the data infrastructure between them hasn't kept up. This role owns the data-to-agent pipeline. You'll ensure that every dataset Manifold touches is not just ingested and governed, but agent-ready: equipped with the schema design, semantic context, and documentation that allow AI to serve researchers effectively. You'll define what "agent-ready" means as a product standard, design the automated onboarding workflows that let us scale from dozens of datasets to thousands, and ensure that governance and provenance become more rigorous as operations move to machine speed. This is a high-impact, hands-on role for someone who sees that AI isn't just a feature layer on top of data infrastructure. It's changing what data infrastructure fundamentally is.

Requirements

5+ years of product management experience, with at least 3 years focused on data platforms, AI infrastructure, or B2B SaaS serving technical users.
Experience in life sciences, genomics, clinical research, or healthcare technology with familiarity in bioinformatics workflows, scientific data types, and regulatory requirements (GxP, HIPAA, 21 CFR Part 11).
Strong understanding of data management fundamentals (ingestion, transformation, governance, quality, lineage) and how these change when AI operates at machine speed.
Experience building products where AI agents are primary consumers of infrastructure, or ability to reason about the interaction between AI capabilities and data architecture.
First principles thinker who reasons from researcher needs and data fundamentals rather than inherited playbooks, comfortable designing for adaptability as AI capabilities evolve.
Strong technical fluency to collaborate with engineering on architecture decisions, API design, and schema modeling without requiring translation.
Scrappy operator who balances zero-to-one innovation with enterprise requirements, comfortable making decisions in ambiguous environments and moving quickly.
High-agency mindset with excellent communication skills and demonstrated ability to conduct user research, translate insights into requirements, and iterate based on feedback.

Responsibilities

Own the product strategy and roadmap for our data platform, defining how datasets become agent-ready across automated ingestion, schema design, governance, and cross-dataset interoperability.
Design the agent-driven data onboarding system, including quality frameworks and human review gates that enable AI to discover, plan, load, and validate datasets at scale.
Define the "agent-ready" standard for datasets: the schema design, semantic context, and validation coverage needed before AI can reliably serve researchers.
Build data governance and compliance capabilities that work at machine speed while meeting requirements for controlled-access datasets and regulated environments (GxP, HIPAA, 21 CFR Part 11).
Partner with researchers, bioinformaticians, and data engineers to validate that AI delivers correct, well-sourced answers to scientific questions.
Prioritize features across academic and commercial users, working with customers, sales, and engineering to identify high-leverage platform investments.
Apply first principles thinking to evolving AI capabilities, continuously reassessing what should be automated versus what requires human judgment.