Intern, AI + Bayesian Decision Intelligence for Clinical Trial Modelling (Oncology)

ABOUT HUMMINGBIRD BIOSCIENCE

Hummingbird Bioscience is a biotherapeutics company working at the interface of artificial intelligence and human innovation to discover and develop transformative medicines for hard-to-treat diseases. Hummingbird Bioscience’s computational and systems biology technologies have generated a pipeline of innovative clinical-stage monoclonal antibodies and antibody-drug conjugates in oncology and autoimmunity. At Hummingbird Bioscience, the commitment to rigorous science, teamwork, and intellectual integrity underpins our passion to accelerate the journey of new drugs from concept to clinic.

For more information, please visit www.hummingbirdbioscience.com, and follow Hummingbird Bioscience on LinkedIn, X (formerly Twitter), and YouTube.

ABOUT THE ROLE

As part of the internship, the intern will be involved in building an AI-enabled Bayesian decision intelligence platform for oncology trials that:

1. Produces interpretable statistical evidence using Bayesian methodology (including borrowing / priors / simulations), and
2. Augments this with machine learning for patient similarity, risk prediction, endpoint modelling; integrated and deployed in our current dashboards and computation infrastructure

This approach directly supports FDA-relevant Bayesian concepts such as prior specification, borrowing, operating characteristics, sensitivity analyses, and transparent reporting.

KEY RESPONSIBILITIES

1. Efficacy & Safety Modelling (AI + Bayesian)

• 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 enrolmen

     2.Data-Driven Priors and Borrowing from External Evidence

• 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

     3.External Control Arm Integration (RWE + Causal ML)

• Build ML-based patient similarity or propensity matching scores and confounding adjustment pipelines to support our use of external controls

     4.Dose-Finding & Adaptive Decision Support

• Develop Bayesian dose-finding models (e.g., model-based or model-assisted approaches) and optimize dose selection balancing efficacy/toxicity

      5.PK data modelling

• Implement methods for population PK and PK-ADA covariate modelling, exposure-adjusted safety and efficacy modelling

       6. LLM-enabled Insight Generation

• 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

       7. Method communication

• Responsible to 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

ESSENTIAL QUALIFICATIONS

• 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

OUR MISSION & VALUES

Our fast-growing biotechnology company is committed to discovering and developing important new drugs for cancer and autoimmune diseases, and living by our values: Excellence, Determination, Teamwork, Intellectual Integrity and Audacity.

JOIN US AT THE FRONTIER OF DRUG DISCOVERY AND DEVELOPMENT

We are looking for passionate and motivated individuals committed to solving important, complex problems. As an intern, you will have the opportunity to learn from a multi-disciplinary team and work towards transforming the therapeutic landscape for hard-to-treat diseases. We are committed to the personal and professional development of our team and offer robust learning and development programs. Hummingbird Bioscience understands the need for flexibility for our team and offers a generous paid time off program with flexibility to support employees through different life stages. We invest in our team’s health and offer a comprehensive and holistic employee assistance program. We foster a more collaborative, productive, and sociable culture with on-site lunches and snacks. Finally, we believe in creating social impact beyond our business through corporate social responsibility initiatives.

Are you ready to join us on our mission to discover and develop important new drugs for cancer and autoimmune disease? Click on ‘Apply for this Job’ to submit your application.

For further enquiries, please email us at [email protected].

Hummingbird Bioscience is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all Singapore and US federal, state and local laws and/or guidelines that prohibit employment discrimination on the basis of age, race, color, gender, sexual orientation, gender identity, ethnicity, national origin, citizenship, religion, genetic carrier status, disability, pregnancy, childbirth or related medical conditions, marital status, protected veteran status and other protected classifications.