Clinical Data Product Owner

The Clinical Data Product Owner is a staff-level individual contributor responsible for defining and owning the vision, roadmap, and quality of clinically oriented data products in oncology. In this onsite Phoenix role, you will act as the clinical voice of the customer, translating clinical research needs into usable patient cohorts, features, validation approaches, and documentation.

Key Responsibilities

• Define and own the vision, roadmap, and quality of clinically oriented data products, creating meaningful concepts and features from genomic, clinical, and pathology data.
• Develop patient eligibility logic, phenotyping algorithms, and inclusion and exclusion criteria that are practical and repeatable across data sources.
• Serve as the clinical voice of the customer by partnering with commercial teams on product strategy and providing scientific expertise during client conversations.
• Lead the design and development of new product features, including management of data-privacy certification workflows such as HIPAA expert determination with third-party providers.
• Partner with data science and engineering to validate cohort definitions, implement clinical requirements, and support product accuracy and usability.
• Establish clinical validation frameworks, quality metrics, and success measures to support product credibility and drive adoption.
• Author scientific and clinical materials, including white papers, vignettes, and presentations, to support evidence generation, customer education, and training.
• Maintain clinical data standards, documentation, and data dictionaries throughout product development.

Required Qualifications

• Advanced degree (PhD, MD, or MSc) in epidemiology, biostatistics, public health, computational biology, or a related clinical or quantitative field.
• 7+ years of experience in clinical research, real-world evidence, or healthcare data product development, including ownership of a data product or data asset.
• Deep, demonstrable understanding of clinical data elements found in claims, electronic medical records, pathology reports, and molecular diagnostics.
• Proven experience with patient phenotyping and clinical cohort development, including translating customer needs into product requirements.
• Strong communication skills to clearly present nuanced clinical and technical information to both scientific and commercial audiences.
• Working proficiency with data tools (SQL and/or Python or R) sufficient to specify and validate cohort logic in collaboration with data science teams.
• Proficiency with Microsoft Office Suite, specifically Word, Excel, Outlook, and general working knowledge of Internet use for business purposes.

Technologies

• SQL
• Python
• R
• Microsoft Word
• Microsoft Excel
• Microsoft Outlook

Preferred Qualifications

• Strong knowledge of oncology, molecular biomarkers, and precision medicine concepts.
• Prior experience in biotechnology, pharmaceutical, or healthcare technology organizations, ideally supporting data partnerships.
• Familiarity with real-world evidence and claims datasets, including de-identification and expert-determination concepts.
• Experience with data governance, data dictionary maintenance, and work requiring clinical data integrity.

Physical Demands

• Works at a computer most of the time, with additional time spent collaborating with colleagues, partners, and business leaders in person or via remote conferencing.
• Requires visual acuity and analytical ability to distinguish fine detail.
• Must be able to sit and/or stand for long periods.

Training

All job-specific, safety, and compliance training is assigned based on the job functions associated with this employee.

Additional Information

• Periodic work-related travel is required (up to 20%).
• Some evenings, weekends and/or holidays may be required.

Conditions of Employment

The individual must successfully complete the pre-employment process, including a criminal background check, drug screening, credit check (applicable for certain positions), and reference verification.