Senior Manager, Data Science (India)

Working with Us Challenging. Meaningful. Life-changing. Those arent 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. Youll 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 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 detailsowning work end-to-end from raw signals to validated outputsspanning time-series QC, preprocessing, artifact handling, imputation, feature engineering, and modeling across accelerometry/actigraphy and cardio-respiratory signals (e.g., HRV, SpO). 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 Youll 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 SpO - 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. Required Qualifications - PhD (preferred) or MS with strong experience in Data Science, Biostatistics, Biomedical Engineering, Computer Science, or related field. - PhD 3-5 years, MS 6-9 years, prior experience working on digital health initiatives within pharma industry, medical devices etc. - Demonstrated hands-on experience with time-series sensor data, including: - QC, preprocessing, artifact handling, imputation, feature engineering for accelerometry/actigraphy - Experience with HRV and/or SpO - Strong Python skills with evidence of shipping code: - Clean, testable code; object-oriented design; modular pipelines - Git/version control, code reviews, and collaborative development practices - Experience with longitudinal statistical modeling for repeated measures data. - Proven ability to translate analytical work into transparent deliverables and communicate results to technical and non-technical stakeholders. Preferred Qualifications (one or more of the following) - Navigational physics experience for movement data and/or biomechanical analysis (a plus): quaternions, Euler angles, dead-reckoning, orientation/heading estimation, etc. - Familiarity with sleep analytics and/or circadian cosinor modeling (or willingness to learn open-source libraries). - Experience managing or integrating third-party analytics (e.g., actigraphy QC workflows) and validating vendor outputs. - Experience with scalable compute and deployment patterns, including: - AWS experience; multi-GPU instances and parallelization for model .