Senior Manager, 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** 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. **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 SpO₂ - 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 clear 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 training/inference **Why Join Us** - Work at the intersection of digital biomarkers, machine learning, and clinical development - Partner with multidisciplinary teams to advance analytics from real-world and trial-based wearable signals - Build practical, validated solutions with high impact—and write the code that delivers them *If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.* **Uniquely Interesting Work, Life-changing Careers** With a single vision as inspiring as “Transforming patients’ lives through science™ ”, every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in a supportive culture, promoting global participation in clinical trials, while our shared values of passion, innovation, urgency, accountability, inclusion and integr