Define and execute a translational ML strategy spanning omics, imaging, and other biomedical data modalities, in close partnership with translational biology, clinical, and ML leadership
Directly contribute to technical work: experimental design, model development, analysis, and interpretation of translational ML outputs in support of active drug programs
Manage a team (initially 2–3 ML scientists) working on imaging and omics applications; hire for emerging needs as the function scales
Translate clinical and translational biology questions into well-posed ML problems, and translate ML results back into actionable recommendations for drug discovery teams
Serve as a strategic voice into the Enchant development roadmap, ensuring that translational use cases, data requirements, and evaluation criteria inform model priorities
Co-design experiments and data collection strategies with translational biology colleagues to maximize the utility of biomedical data for ML modeling
Establish evaluation frameworks that connect ML model performance to clinically meaningful endpoints and decision criteria
Communicate translational ML results and strategy to internal stakeholders, partners, and at scientific conferences

PhD in machine learning, computational biology, bioinformatics, or a related computational STEM field
Demonstrated expertise applying ML methods to biomedical or translational problems, including hands-on experience with omics data and biomedical imaging modalities
Industry experience in drug discovery or pharmaceutical R&D, with exposure to translational workflows such as biomarker discovery, patient stratification, or drug candidate selection
Ability to grapple with conceptual complexities of experimental design across diverse biological data types
Strong communication skills and a track record of working effectively across ML and biology/chemistry teams
Experience managing or building small technical teams

Director, Translational Machine Learning

Key skills