Senior Product and Operations Manager (HYBRID: Chicago, NYC, RTP)

Tempus AI•Chicago, IL

12d•Hybrid

About The Position

Tempus is a technology company advancing precision medicine through the practical application of AI in healthcare. We have built one of the largest libraries of multimodal clinical and molecular data, and an operating system to make that data accessible and useful — helping physicians make real-time, data-driven decisions and accelerating the development of new therapies. The breadth and quality of our data is a core competitive advantage. Realizing that advantage depends on continually bringing new, high-value data sources onto our platform — from genomic and pathology labs to EHR-integrated provider networks, real-world data aggregators, and clinical content partners — and integrating them in a way that is scalable, compliant, and genuinely useful to downstream products. The Role: We're hiring a Senior Product Manager to own the data partnerships and integrations roadmap for the Tempus data platform. You will be the connective tissue between external data partners and our internal platform, clinical, and engineering teams — defining what data we bring on, how we ingest and normalize it, and how we turn it into a reliable, productized asset. This is a high-impact, cross-functional role for someone who is equally comfortable in a partner negotiation, a data model design review, and a roadmap prioritization discussion. You'll work across a diverse partner landscape spanning genetic and oncology testing labs, genomics and sequencing providers, EHR and provider data collaborations, FHIR-based health data platforms, real-world data, and clinical decision support content.

Requirements

5+ years in product management, with experience owning data, platform, or integration products (or comparable experience in data engineering, technical program management, or partnerships with a strong product orientation).
Deep familiarity with healthcare data standards and interoperability (FHIR, HL7, OMOP, ICD/SNOMED/LOINC) and real-world data (RWD/RWE).
A strong technical foundation: comfort with data models, APIs, ETL/ELT pipelines, and data quality concepts, and the ability to engage credibly with engineers on integration design.
Experience working with external partners — translating ambiguous agreements into clear requirements and managing relationships across organizational boundaries.
Demonstrated ability to prioritize ruthlessly and drive cross-functional execution across engineering, legal, business development, and clinical stakeholders.
Excellent written and verbal communication; able to move fluidly between a partner conversation and a technical design discussion.

Nice To Haves

Experience in healthcare, life sciences, genomics, or clinical data.
Working knowledge of HIPAA, data privacy, consent, and de-identification practices.
Experience with cloud data platforms and modern data infrastructure.
Hands-on with SQL or scripting for data exploration and validation.

Responsibilities

Own the data-source roadmap. Define and prioritize which new data sources and internal and external data partners to integrate, balancing strategic value, clinical utility, technical effort, and compliance risk.
Lead cross-functional integration squads. Assemble and coordinate squads comprising Connectivity Engineers, Data Product Engineers, Domain SMEs to execute end-to-end data integrations – from intake through production launch and handoff to Operations.
Partner with external data providers. Work alongside business development and legal to evaluate prospective partners, scope data-sharing arrangements, and translate partnership terms into concrete product and integration requirements.
Drive integrations end to end. Lead the technical onboarding of new data sources — ingestion, mapping to common data models, normalization, quality validation, and ongoing monitoring — in partnership with engineering and data teams.
Define data standards and contracts. Establish requirements for data formats, interoperability standards (e.g., FHIR, HL7), metadata, and provenance so each new source is consistent and trustworthy.
Make data usable downstream. Ensure integrated data is discoverable and well documented for the clinical, product, and research teams who build on the platform; gather their requirements and feed them back into the roadmap.
Measure what matters. Define success metrics for data coverage, completeness, freshness, quality, and adoption, and use them to drive continuous improvement.
Navigate compliance by design. Partner with privacy, security, and regulatory teams to ensure every integration meets HIPAA and applicable data-governance, consent, and de identification requirements.