Associate Director, Data Science
Bristol-Myers Squibb
- Location
- Princeton NJ, US
- Work model
- On-Site
- Level
- Senior
- H-1B history
- 57 approvals (FY2023)
- Posted
- Aug 13, 2026
About this role
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 as a senior scientific and technical leader, driving data science strategy and execution to advance the global drug development process. We are looking for a seasoned data scientist with a strong computational, statistical, and biological background and a demonstrated track record of leading analytical strategy, driving methodological innovation, and translating complex, multi-modal data into impactful scientific insights that inform clinical development decisions. As an Associate Director, you will provide scientific leadership across diverse data types generated in drug development — including clinical trial data, genomics, proteomics, imaging, flow cytometry, and other biomarker modalities — driving both the strategic direction and hands-on execution of data science efforts across early-to-late phase drug development programs. You will define and champion analytical frameworks, methodological standards, and scalable approaches that elevate the quality and impact of data science across the organization, while serving as a key scientific partner to Biostatistics leads, Translational and Clinical Scientists, and senior cross-functional stakeholders. This position may include management of a small team of data scientists. We are looking for a technically excellent, scientifically influential, and strategically minded practitioner.
What You'll Do
Data Science Strategy & Scientific Leadership Serve as a senior scientific resource within the DSAA organization, providing strategic direction and methodological guidance on data science approaches across multiple drug development programs Lead the design and execution of exploratory and confirmatory analyses (both hypothesis-generating and hypothesis-driven) across diverse and complex data types, from early discovery through late-phase clinical development Drive the development and implementation of innovative statistical methods, novel analytical frameworks, and state-of-the-art AI/ML approaches to address key scientific questions in drug development Shape the analytical strategy for drug development programs, contributing to decisions around trial design, endpoint selection, biomarker strategy, and evidence generation Identify opportunities to leverage emerging data science methodologies and technologies to accelerate drug development and address the complexities of novel data types Represent DSAA in cross-functional program team meetings, providing authoritative scientific input and influencing development decisions through rigorous, data-driven analysis Advanced Analytics & Modeling Lead the development and application of novel computational methods for patient segmentation, biomarker discovery, and precision medicine from multimodal clinical and omics datasets in partnership with Translational, Clinical, and Statistical Scientists Oversee and execute data science analyses on datasets from BMS clinical trials and real-world data cohorts,