Director, AI Research

Accountabilities

• Research Strategy and Roadmap: Set and execute a research strategy focused on the highest-impact clinical-development challenges, prioritizing efforts that drive measurable benefits for patients and the pipeline.
• Method Development for Trials: Invent and refine AI and statistical learning methods tailored to randomized clinical trials and related evidence, with emphasis on heterogeneous treatment effects, predictive biomarkers, time-to-event outcomes, patient stratification, and cross-trial generalization.
• Agentic AI Systems: Design, build, and rigorously evaluate agentic AI systems for clinical-development use cases, incorporating retrieval, tool use, code execution, memory, multi-step workflows, and human oversight to ensure reliability and reproducibility.
• Reproducible Engineering: Write high-quality Python and build reproducible research workflows using modern ML frameworks, version control, testing practices, and cloud or high-performance computing environments to accelerate trustworthy science at scale.
• Scientific Leadership and Influence: Serve as a senior scientific and technical partner, shaping clinical strategy, evaluation approaches, platform capabilities, and investment decisions across the organization.
• External Dissemination and Partnerships: Share research through peer-reviewed publications, reusable tools, patents, scientific collaborations, and conference presentations; represent the company with partners, consortia, regulators, and the broader research community.
• Team Culture and Mentorship: Cultivate a rigorous research culture through structured scientific review, constructive challenge, and mentorship of scientists and engineers, raising the bar for impact and integrity.
• Responsible AI and Governance: Establish robust evaluation, governance, and responsible-use frameworks for advanced AI systems in clinical development, aligning innovation with patient safety and regulatory expectations.

Essential Skills/Experience

• Hold a PhD in Computer Science, Machine Learning, Biostatistics, Biomedical Informatics, Computational Biology, or a closely related quantitative field, or have an MSc with an equivalent record of research and industry impact.
• Eight years of experience in original research and technical impact, demonstrated through influential publications, clinical evidence, deployed systems, patents, widely used software or datasets, or comparable contributions.
• Have led substantial research programs end to end and made scientific or technical decisions that shaped a broader portfolio, platform, or organizational direction.
• Are comfortable setting direction under uncertainty, challenging assumptions, and influencing senior partners in our organization
• Bring expertise in relevant research topics, such as unsupervised learning, dimensionality reduction, causal inference, survival or longitudinal analysis, Bayesian modeling, multimodal learning, domain adaptation, model interpretability.
• Are comfortable working with AI agent harnesses, quick to pick up new approaches, and curious about what makes agents useful, reliable, and safe.
• Have strong Python and scientific-computing skills and experience with a modern ML framework such as PyTorch, JAX, or TensorFlow.
• Can explain technical findings, tradeoffs, and limitations to clinical, statistical, regulatory, engineering, and leadership audiences.

Desirable Skills/Experience

• Experience with biomedical data such as clinical text, EHR or trial data, biomedical imaging, genomics, transcriptomics, spatial biology, or real-world data.
• Hands-on experience with large language models or agentic systems, including fine-tuning or post-training, retrieval-augmented generation, tool use, agent harnesses, multi-step orchestration, evaluation, or inference and serving.
• Familiarity with clinical data standards, regulated validation, GxP, software as a medical device, model-risk management, or interactions with health authorities.
• Contributions to scientific consortia, reporting or evaluation standards, open-source communities, or other work that has influenced practice beyond a single project.
• Experience mentoring scientists or engineers and providing technical leadership in a global, matrixed organization.