Scientific Director, Clinical and Real World Evidence

Key Responsibilities

• Serve as scientific and technical lead for clinical and real-world evidence initiatives, contributing subject matter expertise to organizational strategy, project design, and execution.
• Lead and enable agile, cross-functional teams for integrated data projects to inform pipeline and strategic decisions, acting as either/both an individual scientific contributor and project leader.
• Oversee the curation, design and analysis of patient cohorts that leverage clinical, multi-omic, EHR, biobank phenotypic and real-world data to generate actionable insights for drug discovery, biomarker identification, and patient stratification.
• Collaborate closely across functions including other RWD teamsto harmonize data workflows and methodologies and align evidence generation with pipeline needs.
• Communicate findings and strategy effectively to internal leadership, external collaborators, and cross-functional partners.
• Stay abreast of latest methodologies and best practices for real world data, EHR analytics, multi-omic data integration, and related AI-driven approaches.
• Play a key role as a member of the QuIL leadership team, helping to shape QuILs strategic direction and supporting broader organizational initiatives.

Qualifications

• M.D. degree is required; board certification and clinical expertise strongly preferred.
• At least 4 years of hands-on experience in real world evidence (RWE), electronic health record (EHR) analytics, ideally within the pharmaceutical, biotechnology, or academic research environment.
• Demonstrated track record in designing and leading interdisciplinary clinical data science initiatives, including both hands-on scientific contribution and project management.
• Advanced knowledge of data integration strategies and analytical methodologies for combining clinical, genomic, and real world data sources.
• Excellent communication skills, with experience presenting scientific findings to internal and external stakeholders.
• Familiarity in key analytics tools/languages (e.g., R, Python, SQL) is preferred but not necessary.
• Familiarity with regulatory guidance and considerations for RWE and integrated evidence generation.