Senior Manager

Working with Us
Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us.

Position Summary

This is a new position. You will join a cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team to advance the global drug development process. We are looking for a candidate with strong computational, statistical, and biological capabilities and a demonstrated track record of translating complex, multi-modal data into testable hypotheses and actionable insights. This role brings together deep expertise in digital health data science, including wearable and sensor-derived longitudinal data, with broader contributions across genomics, proteomics, imaging, flow cytometry, and other biomarker data types generated from clinical trials.

As a hands-on individual contributor, you will drive exploratory analysis (both hypothesis-generating and hypothesis-driven) for scientific questions related to drug development and clinical study design. You will define approaches, processes, algorithms, and pipelines that support analytics, visualization, and decision support needs of drug development scientists and project teams, while collaborating closely with Biostatistics leads and cross-functional partners across the organization. We are looking for a hands-on, state-of-the-art practitioner.

What You'll Do

Digital Health & Wearable Data Science (Deep Expertise)

• Build and maintain Python pipelines for wearable and sensor-derived time-series data, including QC, preprocessing, sensor artifact removal, imputation, and feature engineering based on clinical concepts of interest
• Develop and validate models for longitudinal sensor data using frequency/time-frequency representations, digital filtering, representation learning, and deep learning approaches (e.g., Transformers, ensembles) with model explainability techniques where appropriate
• Apply statistically rigorous approaches to repeated-measures and longitudinal data, including mixed-effects/hierarchical models and study-appropriate strategies for within-subject dynamics and missingness
• Drive quantitative characterization of physiological and clinically meaningful measures (e.g., accelerometry/actigraphy, HRV, SpO₂) associated with disease progression or patient subtyping
• Collaborate with and perform QC/validation of third-party analytics providers and vendor-derived digital biomarker outputs
• Implement strong evaluation practices and reproducible research standards (nested CV, LOO, OOB methods, structured codebases, version control)

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

• Develop and apply novel or existing computational methods for patient segmentation and biomarker discovery from multimodal clinical, digital health, and omics datasets in partnership with Translational, Clinical, and Statistical Scientists
• Execute data science and biomarker 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
• Partner with lead and protocol statisticians in contributing to statistical analysis plans (SAPs) for exploratory biomarker and digital health analyses, highlighting the data science strategy for clinical drug development
• Perform relevant and innovative statistical analyses of high-dimensional data (e.g., gene expression, sequencing, imaging features) generated by cutting-edge technologies
• Develop novel ways of integrating, mining, and visualizing diverse, high-dimensional, and disparate datasets across early-to-late phase drug development
• Formulate, implement, test, and validate predictive models and imp