We are seeking a collaborative Technical Product Manager to join a cross-functional software delivery team. with deep expertise in pharmaceutical data platforms across discovery, pre-clinical, and clinical domains. This hands-on individual contributor role serves as a critical bridge between Business Analysts, scientists, and engineering teams. The successful candidate excels at diving into the details — mapping complex business and scientific workflows, translating BRDs into detailed Software Requirements Specifications (SRS), defining epics and user stories, and acting as the Product Owner for agile delivery teams, while not losing sight of the bigger picture. This is a technical, execution-focused role responsible for driving data platform initiatives that enable reliable, compliant, and scalable capabilities across research and clinical operations enterprise-wide.
Required Qualifications
- 5+ years of professional experience in Technical Product Management or Product Owner experience in pharmaceutical/biotech data platforms (or equivalent combination of education and experience).
- BS/BA degree in Computer Science, Life Sciences, or related field, Deep domain expertise across discovery, pre-clinical, and clinical domains, including multi-omics, lab instrument data, chemistry informatics, biomarker analytics, and related workflows.
- Proven ability to map detailed workflows, author SRS documents from BRDs, write granular epics/stories, and drive agile delivery as a Product Owner.
- Strong technical acumen with modern data platforms (Databricks, lakehouse, cloud data pipelines, data catalogs, governance tools) and experience working directly with engineering teams, both on-site and remote, on implementation.
- Excellent communication and collaboration skills with the ability to bridge technical and scientific audiences.
Nice to Have Qualifications
- Advanced degree or relevant certifications.
- Prior experience with AWS, Databricks, data mesh principles, semantic layers, or scientific computing tools (LIMS, ELNs).
- Background in AI/ML enablement for research or translational science.
- Interest in or prior exposure to regulated domains (e.g., HealthTech, FinTech, or Life Sciences).