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Strong employer brand plus generalist Data Scientist title but specialized domain reduces applicant density.
Role requires pharma drug-discovery domain expertise and PK/ADME experience, limiting cross-industry transferability.
PhD requirement, explicit 7-8 years and domain-specific PK/ADME expertise make filters highly stringent.
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Partner with PKS wet and dry lab teams and D&D to identify business needs and provide aligned data-driven solutions for drug discovery.
Design, build, and maintain scalable data pipelines, applications, and machine learning models to analyze complex biological and chemical datasets supporting lead optimization.
Act as a data science representative within Integrated Drug Discovery, contributing scientific insights and integrating computational solutions into workflows.
PhD in life sciences, computational biology, cheminformatics, bioinformatics, or related, plus 3-4 years post-PhD or 7-8 years total relevant drug discovery experience in biomedical/pharmaceutical research.
Strong expertise in machine learning, statistics, and data science workflows applied to drug discovery.
Proficiency in Python and/or R with software engineering practices including testing, version control, and documentation.
Experience in pharmacokinetics (PK), ADME, or PK/PD modeling; familiarity with deploying production-grade data products or ML systems.
Experienced data scientist with a strong background in drug discovery and pharmacokinetics-related domains.
Skilled in developing scalable, production-quality computational workflows and integrating ML models into scientific teams.
Strategic collaborator comfortable working cross-functionally with lab teams and digital/data groups to translate complex experimental data into actionable insights.