About the role
AI summarisedJoin Hummingbird Bioscience to build an AI-enabled Bayesian decision intelligence platform for oncology trials. This role applies advanced statistical and machine learning techniques to generate interpretable evidence, predict patient outcomes, and support critical decision-making in drug development.
BiotechOnsite
Key Responsibilities
- Develop Bayesian models for primary and secondary endpoints, including credible intervals and posterior probabilities.
- Implement ML models to predict response and safety based on baseline patient features/biomarkers.
- Construct and justify priors informed by historical trials and Real-World Evidence (RWE), including sensitivity analyses.
- Apply borrowing methods across similar diseases or patient subgroups to enhance modeling power.
- Build ML pipelines for patient similarity matching and confounding adjustment when integrating external control arms.
- Develop Bayesian dose-finding models to optimize dose selection balancing efficacy and toxicity.
- Implement population PK and PK-ADA covariate modelling for exposure-adjusted safety and efficacy assessment.
- Create an internal 'Clinical Insights Copilot' using LLMs to draft reports from modeling results.
- Produce stakeholder-ready presentations explaining the clinical question, modeling approach, assumptions, key results, and recommended actions.
Requirements
- Currently enrolled in a Bachelor’s or Master’s Program in a related quantitative field.
- Strong proficiency in R for clinical statistics and reporting (tidyverse, stat libraries).
- Familiarity with Bayesian modelling concepts: priors, posterior inference, operating characteristics via simulation, and sensitivity analyses.
- ML experience in clinical outcome prediction (e.g., tree-based models, survival ML, calibration/validation).
- Experience with modern AI tooling (LLM/RAG frameworks) and responsible data use.
- Understanding of data privacy and security best practices for patient data.
- Interest in oncology drug development and clinical/biostatistical approaches.
- Excellent communication skills to present complex concepts clearly.