Manager, Data Science & AI Delivery Lead – Clinical Development and Medical Affairs

Key Responsibilities

• Lead discovery with Clinical Development and Medical Affairs stakeholders to define analytical questions, hypotheses, target outcomes, success measures and product requirements.
• Perform hands-on data exploration, data preparation, feature engineering, statistical analysis and model prototyping using clinical, operational, medical and scientific datasets.
• Design, develop and evaluate machine learning, natural language processing and generative AI solutions appropriate to the business problem, data quality and regulated context.
• Establish rigorous experimentation and evaluation approaches, including baseline comparisons, error analysis, robustness testing, explainability, bias assessment and human-in-the-loop review.
• Translate prototypes into production-ready solutions by partnering with data engineering, MLOps, architecture, platform, security, privacy and quality teams.
• Own end-to-end delivery for assigned data science and AI products, including scope, roadmap, backlog, sprint execution, dependencies, release readiness, adoption and continuous improvement.
• Develop epics, user stories, acceptance criteria and validation evidence that connect model performance to user needs and measurable business or scientific outcomes.
• Guide data scientists and delivery partners on analytical methods, coding quality, reproducibility, documentation, model evaluation and responsible AI practices.
• Support development and monitoring of data pipelines, feature stores, model services, APIs and analytical applications using approved enterprise platforms.
• Define and track data quality, model performance, drift, reliability, user adoption, cycle-time improvement and value-realization metrics.
• Ensure solutions comply with applicable data governance, privacy, security, validation and GxP requirements.
• Maintain model documentation, experiment records, technical specifications, operating procedures and production-support runbooks.
• Manage vendors and co-sourced teams, remove delivery blockers, escalate risks and communicate technical trade-offs and progress to stakeholders and leadership.

Skills and Experiences

• Bachelor’s degree in data science, statistics, computer science, engineering, applied mathematics, bioinformatics, life sciences or a related discipline and 6+ years of relevant experience; or a master’s degree and 4+ years of relevant experience.
• Hands-on experience developing statistical, machine learning, NLP or generative AI solutions using Python, SQL and commonly used data science libraries.
• Experience with supervised and unsupervised learning, experimental design, feature engineering, model evaluation, error analysis and interpretation of analytical results.
• Experience delivering data science products from problem definition and prototyping through production deployment, monitoring, adoption and enhancement.
• Working knowledge of data pipelines, cloud platforms, APIs, version control, automated testing, model lifecycle management and MLOps practices.
• Experience leading Agile delivery, including roadmap development, backlog prioritization, sprint planning, release coordination, dependency management and risk escalation.
• Ability to assess data fitness and communicate uncertainty, model limitations, technical trade-offs and recommendations clearly.
• Strong stakeholder management and communication skills across clinical, medical, scientific and technical audiences.

Preferred Qualifications

• Experience applying data science or AI in Clinical Development, clinical operations, clinical data management, biostatistics, safety, regulatory, quality or Medical Affairs.
• Experience with clinical trial, real-world, medical insights, publications, scientific literature or other life sciences datasets.
• Experience in a GxP-regulated environment and familiarity with model documentation, privacy, validation and responsible AI controls.
• Experience with Databricks, AWS, MLflow, vector search, retrieval-augmented generation or large language model evaluation.
• Familiarity with Veeva, clinical trial management systems, electronic data capture platforms, safety systems or CRM platforms.
• Experience leading vendors or co-sourced technical teams and establishing measurable adoption and business-value outcomes.