Senior Manager, Data Science

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it. Position Summary BMS Digital Health is seeking a Senior Manager, Data Science to build and deliver hands-on, code-first analytics and algorithm development using wearable and sensor-derived longitudinal data . This role is for a data scientist who thrives in the details—owning work end-to-end from raw signals to validated outputs—spanning time-series QC, preprocessing, artifact handling, imputation, feature engineering, and modeling across accelerometry/actigraphy and cardio-respiratory signals (e.g., HRV, SpO₂). The ideal candidate enjoys writing production-quality Python in orchestration environments, applying rigorous validation, and collaborating across internal and external partners. This is a highly hands-on individual contributor role. You will spend a significant portion of your time coding, debugging, reviewing PRs, and building reproducible pipelines and models. What You’ll Do (Hands-on Responsibilities) Build and maintain Python pipelines for wearable time-series data, including: QC, preprocessing, and sensor artifact removal Imputation (baseline through advanced methods) and feature engineering based on clinical concepts of interest EDA and signal characterization for accelerometry/actigraphy, HRV, and SpO₂ Signal processing and signal detection Develop and validate models for longitudinal sensor data using: Frequency / time-frequency representations, digital filtering, and representation learning Quantitative characterization of physiological and clinically meaningful measures provably associated with disease progression or subtyping.  Deep learning approaches (Transformers and/or ensembles) with model explainability techniques where appropriate Apply statistically rigorous approaches to repeated-measures data: Longitudinal statistical modeling (e.g., mixed effects / hierarchical models) Study-appropriate strategies for within-subject dynamics and missingness Implement strong evaluation practices and reproducible research standards: Nested CV, LOO, and/or OOB methods where appropriate Reproducible experimentation, documentation, and well-structured codebases Collaborate actively with internal stakeholders (clinical, stats, engineering, product) and external partners / third-party analytics providers, including QC and validation of vendor-derived outputs. Contribute to team excellence via code reviews, technical mentorship (scope depends on level), and raising engineering rigor.