Senior Manager, Data Science

Bristol-Myers Squibb
Posted on
Bristol-Myers Squibb logo
Experience
6 - 9 yrs
Job Location
Hyderabad, India
Vacancy
1
Designation
Senior Data Science Manager
Job Type
ONSITE

Job Description

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, 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 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 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 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
Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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