About the role
AI summarisedIntern role at a biotherapeutics company to build an AI-enabled Bayesian decision intelligence platform for oncology clinical trials. The intern will develop Bayesian models, implement ML for patient similarity and risk prediction, and create an LLM-powered clinical insights copilot.
BiotechFull-time
Key Responsibilities
- Develop Bayesian models for primary and key secondary endpoints (e.g., response, PFS/OS, safety event rates), including credible intervals and posterior probabilities as decision summaries
- Implement ML models to predict response and safety based on baseline features/biomarkers collected during patient screening process, which may inform our inclusion/exclusion criteria for enrolment
- Construct and justify priors informed by historical trials and RWE; quantify prior influence (e.g., effective sample size) and run sensitivity analyses across alternative priors
- Implement borrowing methods across similar diseases or disease subtypes, and borrowing information between subgroups of a patient population
- Build ML-based patient similarity or propensity matching scores and confounding adjustment pipelines to support our use of external controls
- Develop Bayesian dose-finding models (e.g., model-based or model-assisted approaches) and optimize dose selection balancing efficacy/toxicity
- Implement methods for population PK and PK-ADA covariate modelling, exposure-adjusted safety and efficacy modelling
- Create an internal 'Clinical Insights Copilot' to draft reports such as weekly signal summaries or executive-ready briefs from the results of our modelling approaches
- Produce stakeholder-ready slide decks to clearly explain: (1) the clinical question (2) the modeling approach with intuitive visuals, (3) assumptions + limitations (4) key results with uncertainty, and (5) recommended actions to both technical and non-technical audience
Requirements
- Currently enrolled in a Bachelor's or Master's Program in a related field
- Possess strong R skills for clinical statistics and reporting (tidyverse, stat libraries)
- Familiarity with Bayesian modelling concepts: priors, posterior inference, operating characteristics via simulation, sensitivity analyses, documentation
- ML experience for clinical outcome prediction (tree-based models, xgboost, survival ML, calibration/validation, interpretability)
- Experience with modern AI tooling (LLM/RAG frameworks) and responsible use for patient and sensitive data (human-in-the-loop, auditability)
- Experience with LLM / AI frameworks for the development of novel analytics
- Understanding of data privacy and security best practices for patient data
- Interest in oncology drug development and to clinical/biostat approaches
- Excellent communication skills to present complex concepts in a clear manner
- Able to commit for a 20 week internship period