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Tier-1 employer and metro location increase competition, while niche computational biology specialization reduces applicant pool.
Role requires specialized computational biology, clinical trial, and translational experience, limiting cross-industry transferability.
Explicit years, degree conditionality, and rigorous publication/technical evidence requirements make screening highly selective.
Design and implement predictive and prognostic biomarker models integrating multi-omic and clinical trial data to inform disease stratification, efficacy, safety, and adverse outcomes.
Develop and maintain AI and machine learning analytical frameworks, including foundation models and generative AI, for scientific workflows in translational medicine.
Collaborate cross-functionally to translate clinical and biological questions into analytical strategies, ensuring reproducibility and production-readiness of analytical methods within global, regulated development programs.
Doctorate in Bioinformatics, Computational Biology, Statistics, Mathematics, Computer Science, Data Science, or related quantitative field with 1-2 years experience OR Master’s degree with 5 years experience.
6+ years of industry experience in relevant roles.
Proven expertise in Python and R for scientific computing and modeling; experience with ML/DL libraries such as PyTorch, TensorFlow, scikit-learn, or tidymodels.
Work Experience Required: At least 6 years of relevant industry experience.
Demonstrated depth in developing and owning advanced multi-modal statistical and machine learning models applied to biomedical or clinical datasets influencing scientific or development decisions.
Experience applying AI, foundation models, and generative AI in regulated biomedical or pharmaceutical environments with an understanding of clinical relevance and data modalities (e.g., NGS, proteomics, imaging).
Prior work experience in large, global biotech or pharmaceutical companies with ability to operate across global teams and align complex interdisciplinary projects.