Senior Manager, Data Sciences

Working with UsChallenging. 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.Drive Insight at the Cutting Edge of Drug DevelopmentAre 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.What You'll DoData Science & AnalyticsDevelop 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 ScientistsExecute 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 typesDevelop innovative approaches to integrating, mining, and visualising diverse, high-dimensional, and disparate datasets generated across early-to-late phase drug developmentFormulate, implement, test, and validate predictive models and build efficient, automated processes for delivering modelling results at scaleApply modern machine learning capabilities — including AI/ML, deep learning, NLP, causal ML, and explainable AI — across multiple data modalities and clinical development contextsApply statistically rigorous approaches to clinical trial data, including survival analysis, longitudinal/mixed-effects modelling, and appropriate handling of missing data and censoringContribute to the scientific and statistical strategy of drug development programs, including the development of predictive biomarkers, novel trial designs, and precision medicine approachesData Engineering & ReproducibilityBuild and maintain well-structured, reproducible, version-controlled analytical pipelines and codebases using Python, R, SQL, and cloud platformsDevelop and apply data quality frameworks to assess and ensure fitness-for-purpose of diverse data sources for specific analytical questionsImplement strong model evaluation practices including cross-validation strategies, calibration assessment, and transparent reporting of model performance and limitationsBuild scalable, automated processes for delivering analytical results across multiple programs and data typesCollaboration & Technical ContributionPartner with lead and protocol statisticians in contributing to statistical analysis plans (SAPs) for exploratory data science analyses supporting drug development programsCollaborate with cross-functional teams including clinicians, translational medicine scientists, biostatisticians, data engineers, and IT/engineering professionalsContribute to team excellence through code reviews, technical mentorship, and raising the overall engineering and methodological standards of the teamCommunicate analytical strategies and results clearly and effectively to both technical and non-technical stakeholders, with strong data presentation and visualisation skillsManage and coordinate deliverables across concurrent, fast-paced projects within tight timelinesWhat We're Looking ForRequired Qualifications:PhD in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, or Computer Science) with 1+ years of academic/industry experience; or a Master's Degree in a relevant quantitative field with 3+ years of industry experienceStrong experience in data science and statistical analysis using clinical trial or electronic health records data, particularly in a pharma R&D contextExperience developing and validating statistical and machine learning models on high-dimensional data for time-to-event, longitudinal, and multivariate outcomesExperience in the application of AI/ML and proficiency in Python, R, SQL, and cloud platforms (e.g., AWS, Azure, Databricks)Familiarity with clinical trial design, drug development processes, and the role of biomarkers in regulatory and clinical decision-makingA perspective on leveraging innovative approaches to expedite drug development and address the complexities of emerging data typesStrong problem-solving, collaboration, and communication skills, with the ability to handle several concurrent, fast-paced projects independently and as part of a teamPreferred Qualifications:Experience with genomics, proteomics, imaging, flow cytometry, or immunobiology datasets from clinical trialsExperience with NLP, causal ML, explainable AI, and survival analysis/time-to-event modellingKnowledge of molecular biology and understanding of disease pathwaysExperience with real-world data (RWD/RWE) sources and associated analytical methodsFamiliarity with digital health data an