Overview
Designs and advances AI- and analytics-driven frameworks to derive translational and reverse-translational insights from clinical trial data using computational biology methods.
What you'll do
- Design and implement predictive and prognostic biomarker models for clinical trial and biomarker data.
- Build multi-omic integration frameworks to jointly analyze molecular modalities and clinical covariates.
- Apply advanced statistical methods including longitudinal/mixed-effects models and survival analysis.
- Develop and evaluate ML, deep learning, and causal inference models for biological and clinical data.
- Contribute to AI-enabled analytical systems, including foundation/large-language-model approaches, generative models, and agentic AI systems.
- Ensure analytical methods are reproducible, scalable, and production-ready with platform and engineering teams.
What you'll need
- Doctorate in a relevant quantitative field with 1–2 years of relevant experience, or a Master’s degree with 5 years of relevant experience.
- At least 6+ years of industry experience.
- Ability to develop models with depth in applied modeling, multi-omic analytics, and AI systems.
- Demonstrated Python and R experience for scientific computing and modeling.
- Experience with modern ML/DL libraries and frameworks such as PyTorch, TensorFlow, scikit-learn, or tidymodels.
- Understanding of drug development and clinical trial data, including biomarker strategies and endpoint definitions.
- Experience with real-world clinical or biomarker data, including QC, preprocessing, feature engineering, integration, modeling, and interpretation.
Nice to have
- Verifiable evidence such as peer-reviewed publications and/or GitHub repositories with substantive modeling contributions.
- Experience applying or extending generative AI, foundation models, or agentic systems for scientific/analytical use cases.
- Prior experience in large global biotech or pharmaceutical organizations.
Details
- Collaborates with biomarker scientists, clinicians, biostatisticians, and data engineers across global teams and time zones.
- Requires working with global stakeholders across time zones with flexible support for global programs.
Read the full description and apply on the company’s own careers page.
