We are seeking a highly skilled professional to join our Real World Data (RWD) team. This role focuses on building scalable, reproducible data pipelines and disease-specific datasets that power clinical research, health economics, and outcomes studies. You will collaborate closely with subject matter experts (SMEs), clinicians, and data engineers to translate protocol-level specifications into computable datasets that support oncology and specialty disease research.

Key Responsibilities :

- Clinical rule implementation : Translate SME-designed clinical rules into scalable, reproducible data pipelines operating against the central data lake.

- Structured and unstructured data integration : Engineer patient-level features using medical and pharmacy claims, lab results, and NLP-derived outputs.

- Disease-specific dataset development : Build and maintain datasets covering cohort construction, index dating, treatment sequencing, and clinical event labeling.

- Line of therapy algorithms : Develop and apply algorithms that handle real-world complexity including combination regimens, gaps, dose modifications, and off-label use.

- NLP signal integration : Incorporate diagnosis mentions, staging, biomarker results, and progression language alongside structured claims to enrich dataset completeness.

- Data quality assurance : Produce data quality reports and conduct sample-level audits to validate clinical logic against source data.

- Collaborative refinement : Work iteratively with SMEs to surface anomalies, identify rule gaps, and refine logic collaboratively.

Required :

- 4+ years of experience in data science, biostatistics, or health data engineering within life sciences.

- Proficiency in SQL and Python (or R) for large-scale healthcare data manipulation.

- Direct experience with claims data, EMR data, or both in an RWD or HEOR setting.

- Familiarity with NLP outputs and integrating unstructured signals into structured datasets.

- Experience implementing clinical event algorithms from protocol-level specifications.

Preferred :

- Experience with cloud data lakes or warehouses (Snowflake, Redshift, Databricks).

- Familiarity with OMOP CDM or other clinical data models.

- Background in oncology datasets or specialty disease datasets.

- Comfort working in a pod model with close clinical collaboration.

Disease Areas :

- Initial pod coverage will include Breast Cancer, Gastric Cancer, and Multiple Sclerosis, with additional indications prioritized based on client demand. Team members will develop deep expertise in anchor indications while expanding breadth across the portfolio.