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Tier-1 pharma, mid-level experience requirement, metro location, and broad skillset increase applicant competition.
Clinical trial, CDISC, and regulatory experience make the role highly industry-specific and less transferable.
Explicit degree and years plus regulated clinical, CDISC, and specific tooling requirements necessitate strict filtering.
Lead clinical data science activities for assigned studies to ensure timely, high-quality analyses and quantitative insights supporting clinical development.
Collaborate cross-functionally to produce analysis-ready, submission-quality data leveraging statistical and AI/ML methods with compliance to regulatory standards.
Drive continuous improvements in analytic workflows through automation, reusable code, and technology adoption while mentoring junior team members.
PhD in quantitative science with 5+ years relevant experience or MS with 7+ years; equivalent combinations reviewed with HR.
Experience in clinical data science within pharmaceutical, biotech, healthcare research, or regulated clinical development environments.
Strong hands-on skills in R and/or Python; working knowledge of SAS and SQL; proficiency in CDISC standards and submission-oriented data handling.
Work Experience Required: 5+ years with PhD or 7+ years with MS in relevant field (clinical data science).
Deep technical expertise in clinical trial design, statistics (longitudinal analysis, survival, causal inference), and drug development analytics.
Experience integrating diverse clinical and high-dimensional data types (biomarker, real-world, imaging, digital health) for quantitative decision support.
Experienced in regulated environments with solid understanding of FDA, EMA, ICH-GCP, GxP standards, data privacy, and clinical submission requirements.