Senior Clinical Data Modeler

• 5+ years of oncology data modeling experience.
• Experience working with real world data from various sources (e.g., curation workflows, EHRs, lab systems, claims, research datasets).
• Experience working with modern ELT tools such as DBT to maintain high volume, high velocity data warehouses.
• Experience working with standard medical terminologies (e.g., SNOMED CT, RxNorm, LOINC, ICD-9/10).

• Clinical background (MD/DO, PharmD, PA, NP, RN, etc.).
• Familiarity with next-generation sequencing data.
• Experience with real world data analysis in Python or R.
• Participation in professional organizations (e.g., OHDSI, AMIA).

We're looking for an experienced Clinical Data Modeler to join a team working at the intersection of oncology, data engineering and real-world evidence.

In this role, you'll work closely with medical oncologists, research scientists and engineers to design and maintain the clinical data models that power products, analyses and data deliveries. You'll take data from EHRs, lab systems, curated datasets and other real-world sources and turn it into structured, reliable and meaningful information, using standard medical terminologies such as SNOMED CT, RxNorm, LOINC and ICD, together with modern ELT tools like dbt.

If you have solid experience in oncology data modeling, enjoy translating clinical and business needs into robust data logic, and care about data quality, this is a chance to have a real impact in a field that matters.

• Collaborate with cross-functional teams including medical oncologists, research scientists, and engineers to develop and maintain clinical data models.
• Translate customer needs and product requests into key concept definitions and business logic for complex models.
• Facilitate integration of data model into workflows, applications, and data deliveries.
• Structure and normalize data from a variety of sources, including curated data, EHR integrations, and lab systems.
• Develop and maintain knowledge bases for clinical concepts.
• Create and execute validation plans in conjunction with SMEs for new models and disease types.
• Proactively monitor and support quality assurance and process improvement initiatives.
• Monitor performance of production processes and recommend areas for improvement.