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Strong employer brand increases applicants, but computational biology specialization and publication requirements limit applicant density.
Highly domain-specific clinical, translational, and multi-omic expertise required, limiting cross-industry transferability.
Requires advanced degree, explicit 6+ industry years, publications/reproducible code, and domain-specific multi-omic expertise.
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Design and implement predictive and prognostic biomarker models integrating multi-omic and clinical trial data for disease stratification, efficacy, safety, and adverse event outcomes.
Develop and advance AI-driven and machine learning analytical frameworks including generative, foundation, and agentic AI models applied to biomedical and clinical data.
Collaborate across global teams to translate biological and clinical questions into analytical strategies and ensure production-ready, scalable, and scientifically interpretable models that impact precision medicine decisions.
Doctorate in Bioinformatics, Computational Biology, Statistics, Mathematics, Computer Science, Data Science, or related quantitative discipline with 1–2 years relevant experience OR Master’s with 5 years relevant experience.
6+ years of industry experience.
Proven expertise in Python and R for scientific computing and modeling.
Work Experience Required: 6+ years in industry in computational biology, bioinformatics, or related fields relevant to biomarker modeling and clinical data analytics.
Deep applied expertise in multi-modal biomedical or clinical data modeling demonstrated by peer-reviewed publications, substantial code repositories, or documented industry impact.
Strong experience building analytical frameworks with advanced ML/DL techniques in regulated environments with translational relevance to clinical development.
Proven track record of end-to-end ownership of analytical projects influencing scientific or drug development decisions, ideally in large global biotech or pharmaceutical organizations.