Associate Director, Data Science

Job description

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, spanning genomics, proteomics, imaging, flow cytometry, and other high-dimensional biomarker data types Drive the integration, mining, and visualization of diverse, high-dimensional, and disparate datasets across therapeutic areas and development phases, developing novel analytical approaches where existing methods fall short Lead the formulation, implementation, testing, and validation of predictive models and scalable automated processes for delivering modeling results across multiple programs Apply and advance the use of AI/ML, deep learning, NLP, causal ML, and explainable AI across multiple data modalities and clinical development contexts, maintaining currency with the state of the art Lead application of rigorous statistical approaches to clinical trial data, including survival analysis, longitudinal/mixed-effects modeling, causal inference, and principled handling of missing data and censoring 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 Data Engineering & Reproducibility Define and champion standards for scalable, reproducible, and well-documented analytical pipelines and codebases using Python, R, SQL, and cloud platforms Establish and enforce data quality frameworks to assess and ensure fitness-for-purpose of diverse data sources across programs Promote rigorous model evaluation practices including appropriate cross-validation, calibration assessment, out-of-sample validation, and transparent reporting of model performance Drive adoption of scalable, automated analytical processes and best-in-class software engineering practices across the team Leadership, Mentorship & Cross-Functional Influence If applicable, manage and develop a small team of data scientists, building capabilities, fostering scientific rigor and innovation, and ensuring delivery of high-quality outputs within program timelines Mentor and provide technical guidance to junior and mid-level data scientists, elevating team-wide methodological and engineering standards through code reviews, collaborative problem-solving, and knowledge sharing Partner with lead and protocol statisticians in shaping statistical analysis plans (SAPs) for exploratory data science analyses supporting drug development programs Collaborate with and influence cross-functional teams including clinicians, translational medicine scientists, biostatisticians, data engineers, regulatory scientists, and IT/engineering professionals Communicate complex analytical strategies and results with clarity and scientific authority to both technical and non-technical audiences, including senior leadership Build and maintain strong, high-trust working relationships across the organization, establishing DSAA as a valued scientific partner Key Requirements Ph.D. in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, Computer Science, or related field) and 6+ years of academic/industry experience; or Master's Degree in a relevant quantitative field and 8+ years of industry experience Demonstrated mastery in data science and statistical analysis with data generated from clinical trials or electronic health records, with a strong track record of delivering impactful results in a pharma R&D context Significant experience leading the development and application of statistical and machine learning models on high-dimensional data for time-to-event, longitudinal, and multivariate outcomes Proven expertise in the application of AI/ML and proficiency in Python, R, SQL, and cloud platforms (e.g., AWS, Azure, Databricks) Deep familiarity with clinical trial design, drug development processes, and the role of biomarkers and data science in regulatory and clinical decision-making Demonstrated ability to define and drive analytical strategy across multiple concurrent programs, balancing scientific rig