Data Scientist, Real World Data

We are seeking a highly skilled Data Scientist to join the Real World Data (RWD) team. This role is pivotal in building scalable, reproducible data pipelines and disease-specific datasets that power critical clinical research, health economics, and outcomes studies. You will collaborate closely with subject matter experts, clinicians, and data engineers to translate complex protocol-level specifications into computable datasets. The ideal candidate will contribute significantly to oncology and specialty disease research by integrating diverse data sources and developing sophisticated algorithms.

Our client is a prominent computer software company known for developing innovative solutions that drive progress across various industries. They are recognized for their commitment to leveraging technology to solve complex challenges and enhance data-driven decision-making. With a focus on cutting-edge research and development, they foster a collaborative and dynamic work environment that encourages continuous learning and professional growth. This organization is dedicated to building scalable and impactful products that serve a global client base.

Responsibilities

• Translate SME-designed clinical rules into scalable, reproducible data pipelines operating against a central data lake.
• Engineer patient-level features by integrating structured and unstructured data, including medical/pharmacy claims, lab results, and NLP-derived outputs.
• Develop and maintain disease-specific datasets covering cohort construction, index dating, treatment sequencing, and clinical event labeling.
• Design and apply line of therapy algorithms to handle real-world complexities such as combination regimens, gaps, dose modifications, and off-label use.
• Incorporate NLP signals like diagnosis mentions, staging, biomarker results, and progression language to enrich dataset completeness.
• Produce data quality reports and conduct sample-level audits to validate clinical logic against source data.
• Work iteratively with subject matter experts to surface anomalies, identify rule gaps, and collaboratively refine logic.

Requirements

• 3.5+ 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.
• Experience with cloud data lakes or warehouses (e.g., Snowflake, Redshift, Databricks) is a plus.
• Familiarity with OMOP CDM or other clinical data models is preferred.
• Background in oncology datasets or specialty disease datasets is advantageous.