Senior Manager, Data Science
Bristol Myers Squibb (company site, opens in a new tab)6–9 yrsHyderabadOn-siteDirect career pageEst. 30–45 LPA
Posted on 10 Sept
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Job description
Structured overview of role & requirements
About This Role
- Lead end-to-end development and validation of analytics pipelines for wearable and sensor-derived longitudinal time-series data including preprocessing, artifact handling, imputation, feature engineering, and advanced modeling.
- Write and maintain production-quality Python code for accelerometry/actigraphy, HRV, and SpO₂ data, ensuring reproducibility, rigorous validation, and code reviews.
- Collaborate with cross-functional teams and external partners to deliver clinically meaningful models and maintain quality and validation of third-party analytics outputs.
Minimum Requirements
- PhD (preferred) or MS in Data Science, Biostatistics, Biomedical Engineering, Computer Science, or related field.
- 3-5 years post-PhD or 6-9 years post-MS experience working on digital health initiatives involving time-series sensor data in pharma or medical devices.
- Strong hands-on Python programming skills with experience shipping clean, testable, modular code; experience in version control and code review.
- Demonstrated experience with time-series sensor data processing (QC, artifact handling, imputation, feature engineering) including accelerometry/actigraphy, HRV, and/or SpO₂, plus longitudinal statistical modeling for repeated-measures data.
Ideal Candidate Profile
- Highly technical, hands-on data scientist who codes daily and develops production pipelines for longitudinal wearable sensor data.
- Experienced in both signal processing and advanced modeling (including deep learning and longitudinal statistical methods) with knowledge of physiological signals related to clinical endpoints.
- Comfortable working in collaborative environments involving internal multidisciplinary teams and external vendors, emphasizing rigor in validation and reproducibility.