Senior Data Scientist (AI-assisted Clinical Development)

The Opportunity

The IA Senior Data Scientist plays a pivotal role in building and deploying AI/ML-powered digital solutions that transform how we develop medicines. You will partner closely with product managers, software engineers, and UX researchers to design, test, and scale statistical capabilities that unlock actionable insights from clinical, operational, and real-world data. With a strong product-thinking mindset and deep technical fluency, you will help create intelligent tools that are scalable, ethical, and built for impact in regulated healthcare environments.

• Apply advanced statistical and machine learning methods to support development of tools and software products for evidence generation, trial design, and decision support across R&D initiatives
• Act as the subject matter expert to inform software products for the conduct of simulation studies and comparative evaluations of analytical approaches for clinical and operational scenarios
• Collaborate with domain experts to translate scientific problems into data science workflows, identifying patterns of workflows that can be automated and productized
• Build and maintain model development pipelines, ensuring traceability, performance monitoring, and reproducibility
• Contribute to the integration of models and algorithms into production applications, working closely with engineering and product teams
• Participate in the design of compliant and interpretable ML product architectures for clinical development environments
• Design documentation templates that communicate modeling decisions, trade-offs, and performance summaries to cross-functional stakeholders
• Contribute to internal knowledge sharing, peer reviews, and community learning within the data science function

Who you are

• Master's or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Bioinformatics, or a related field
• 3+ years of experience applying machine learning and statistical modeling to data-centric problems in research or product environments
• Proficient with Python or R, including experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch
• Experience in experimental design, simulation, and evaluation of model performance
• Ability to translate scientific or business questions into model architectures and data pipelines
• Practical experience with version control, testing frameworks, and collaborative software development
• Experience working with one or more of the following: RWD/RWE, decision analysis, clinical biomarkers, Bayesian inference, or interpretable ML
• Attention to detail and quality work with an ability to manage and prioritize multiple projects simultaneously
• Excellent collaboration skills, including statistical consulting skills, interpersonal skills, ability to influence others without authority, and ability to build strong collaborative relationships
• Capacity for independent thinking and ability to make decisions based upon sound principles
• Excellent strategic agility including problem-solving and critical thinking skills
• Respect for cultural differences when interacting with colleagues in the global workplace
• Excellent verbal and written communication skills, with the ability to explain complex technical concepts in clear language

Preferred

• Experience contributing to ML product pipelines and deployment workflows
• Experience building evidence synthesis models (e.g., network meta-analysis) or constructing data-driven priors for drug development
• Experience with Bayesian computing or probabilistic programming languages (PPLs), including Stan, PyMC, brms, or others
• Experience with collaborative coding practices and modern version control (e.g., git, CI/CD)
• Experience with generative AI tooling for software development (e.g., agentic coding, spec-driven development, AI code reviews)
• Experience developing agentic AI tooling for data analysis or processes relevant to the pharmaceutical industry
• Knowledge of compliance-related standards (e.g., GxP, HIPAA)
• Publication or public presentation experience in applied statistics or machine learning
• Experience working in agile, cross-functional teams with scientific or product stakeholders