Director, Data Scientist, IT​/Tech

Overview

This is a new position. You will join a cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team to drive and shape the global drug development process. We are looking for a seasoned, scientifically accomplished data scientist with a demonstrated track record of leading analytical strategy, translating complex multi-modal data into impactful scientific insights, and influencing drug development decisions. This role brings together deep expertise in digital health data science, including wearable and sensor-derived longitudinal data, with strategic and hands-on contributions across genomics, proteomics, imaging, flow cytometry, and other biomarker data types generated from clinical trials.

As a Director, you will serve as a recognized scientific and technical leader within the DSAA organization, driving the vision, methodology, and execution of exploratory data science efforts across early-to-late phase drug development. You will define and champion approaches, frameworks, algorithms, and platforms that support the analytics, visualization, and decision support needs of drug development scientists and project teams. This position may include management of a small team of data scientists.

You will collaborate closely with Biostatistics leads, Translational and Clinical Scientists, and other senior cross-functional partners to ensure that data science work is fully integrated into the development, regulatory, and commercial strategy for BMS products.

What You'll Do

Digital Health & Wearable Data Science (Deep Expertise)

• Lead the strategy and hands-on execution of data science efforts for wearable and sensor-derived time-series data, including the design and oversight of Python pipelines for QC, preprocessing, sensor artifact removal, imputation, and feature engineering based on clinical concepts of interest
• Drive development and validation of advanced models for longitudinal sensor data, spanning frequency/time-frequency representations, digital filtering, representation learning, and deep learning approaches with rigorous model explainability
• Define and implement statistically rigorous approaches to repeated-measures and longitudinal data, including mixed-effects/hierarchical models and principled strategies for within-subject dynamics and missingness
• Lead quantitative characterization of physiological and clinically meaningful measures associated with disease progression, patient subtyping, or treatment response, with an eye toward regulatory-grade evidence generation
• Oversee and critically evaluate third-party analytics providers and vendor-derived digital biomarker outputs, ensuring scientific validity and fit-for-purpose quality
• Champion strong evaluation standards, reproducible research practices, and scalable engineering principles across digital health work streams

Broader Multi-Modal Data Science (Clinical Trial & Drug Development)

• Drive the scientific strategy and execution of data science and biomarker analyses across multimodal clinical, digital health, and omics datasets, including genomics, proteomics, imaging, flow cytometry, and other high-dimensional biomarker data types from BMS clinical trials and real-world cohorts
• Lead and shape the development and application of novel computational methods for patient segmentation, predictive biomarker discovery, and precision medicine in partnership with Translational, Clinical, and Statistical Scientists
• Provide senior scientific input into statistical analysis plans (SAPs) for exploratory biomarker and digital health analyses, shaping the data science strategy for clinical drug development programs
• Perform and oversee innovative statistical analyses of high-dimensional data generated by cutting-edge technologies, ensuring methodological rigor and interpretability
• Lead the integration, mining, and visualization of diverse, high-dimensional datasets across therapeutic areas and development phases, developing novel analytical frameworks where existing approaches fall short
• Drive the formulation, implementation, testing, and validation of predictive models and scalable automated processes for delivering modeling results across programs
• Champion the application of modern machine learning capabilities, including AI/ML, deep learning, NLP, causal ML, and explainable AI, to accelerate drug development
• Contribute to and influence the scientific and statistical strategy of drug development programs, including the development of predictive biomarkers, novel trial designs, and precision medicine approaches

Leadership, Strategy & Cross-Functional Influence

• Serve as a senior scientific thought leader within the DSAA organization, shaping the vision and roadmap for digital health and multi-modal data science in drug development
• If applicable, manage and develop a small team of data scientists, building capabilities, fostering a culture of rigor and innovation, and ensuring delivery of high-quality work
• Build and sustain strong partnerships with senior…