Senior Manager, Data Sciences

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits.

This is work that transforms the lives of patients, and the careers of those who do it.

Are you a hands-on data scientist with a passion for turning complex, multi-modal data into actionable insights that shape clinical decisions? Bristol Myers Squibb is seeking a Senior Manager, Data Science to join our Drug Development Data Science & Advanced Analytics (DSAA) team.

This is a new role for a state-of-the-art individual contributor who thrives at the interface of computational science, statistical rigour, and drug development
. You will execute and drive exploratory and confirmatory analyses across a rich variety of data types — from clinical trial data to genomics, proteomics, imaging, and beyond — contributing directly to decisions that advance our global development pipeline.

Data Science & Analytics

• Develop and apply novel computational methods for patient segmentation, biomarker discovery, and hypothesis generation from multimodal clinical and omics datasets, in partnership with Translational, Clinical, and Statistical Scientists
• Execute data science analyses on datasets from BMS clinical trials and real-world data cohorts, spanning genomics, proteomics, imaging, flow cytometry, and other high-dimensional biomarker data types
• Develop innovative approaches to integrating, mining, and visualising diverse, high-dimensional, and disparate datasets generated across early-to-late phase drug development
• Formulate, implement, test, and validate predictive models and build efficient, automated processes for delivering modelling results at scale
• Apply modern machine learning capabilities — including AI/ML, deep learning, NLP, causal ML, and explainable AI — across multiple data modalities and clinical development contexts
• Apply statistically rigorous approaches to clinical trial data, including survival analysis, longitudinal/mixed-effects modelling, and appropriate handling of missing data and censoring
• Contribute to the scientific and statistical strategy of drug development programs, including the development of predictive biomarkers, novel trial designs, and precision medicine approaches

Data Engineering & Reproducibility

• Build and maintain well-structured, reproducible, version-controlled analytical pipelines and codebases using Python, R, SQL, and cloud platforms
• Develop and apply data quality frameworks to assess and ensure fitness-for-purpose of diverse data sources for specific analytical questions
• Implement strong model evaluation practices including cross-validation strategies, calibration assessment, and transparent reporting of model performance and limitations
• Build scalable, automated processes for delivering analytical results across multiple programs and data types

Collaboration & Technical Contribution

• Partner with lead and protocol statisticians in contributing to statistical analysis plans (SAPs) for exploratory data science analyses supporting drug development programs
• Collaborate with cross-functional teams including clinicians, translational medicine scientists, biostatisticians, data engineers, and IT/engineering professionals
• Contribute to team excellence through code reviews, technical mentorship, and raising the overall engineering and methodological…