Q
Director of AI and Algorithms
HybridFull-timeAI & Machine LearningHealthcare TechnologyClinical Research
Lead the AI and Algorithms team developing machine learning models and methods for clinical trial simulation, prediction, and optimization. Set scientific direction and guide work from research through production, collaborating across clinical, data, product, and engineering teams. The team works in
Responsibilities
- Lead and grow a team of researchers, data scientists, and algorithm developers.
- Set the scientific and algorithmic roadmap for simulation, prediction, and AI products.
- Guide development of foundation models, predictive models, and simulation methods.
- Establish validation, benchmarking, uncertainty quantification, and model calibration approaches.
- Collaborate with Clinical Informatics, DataOps, MLOps, Product, and Engineering teams.
- Evaluate and adopt machine learning methods, data representations, and modeling approaches.
- Guide major modeling, statistical, and methodological decisions.
- Oversee scientific publications, conference submissions, and external research collaborations.
- Represent the company’s scientific capabilities to customers, partners, investors, and the wider AI and healthcare communities.
- Mentor senior technical talent and foster scientific rigor, innovation, and execution.
- Work in a hybrid arrangement, with most of the team in the office two days per week.
Requirements
- Experience leading AI, machine learning, or applied research teams in production environments.
- Strong background in machine learning, statistics, predictive modeling, or a related quantitative discipline.
- Experience developing, evaluating, and deploying machine learning systems for real-world products or business outcomes.
- Understanding of model validation, experimental design, uncertainty quantification, and statistical evaluation.
- Ability to turn ambiguous scientific or business problems into practical modeling solutions.
- Experience recruiting, mentoring, and developing technical teams.
- Strong communication and cross-functional collaboration skills.
- Ability to balance scientific innovation with product and business needs.
Nice to have
- Experience with foundation models, large-scale machine learning, or AI-first products.
- Experience with healthcare, life sciences, biomedical, or real-world patient data.
- Scientific publications, conference presentations, or external thought leadership.
- Experience with simulation, causal inference, time-to-event models, or advanced predictive modeling.
- Experience in a high-growth startup.