Senior Manager, Data Science
Company: Bristol Myers Squibb (BMS)
Location: Hyderabad, Telangana, India
Department: Digital Health / Data Science
Job Type: Full-Time (Hybrid)
JOB OVERVIEW
The Senior Manager, Data Science is responsible for developing advanced data science solutions for digital health initiatives by analyzing wearable and sensor-derived longitudinal clinical data. The role involves building end-to-end Python pipelines, performing signal processing, feature engineering, machine learning model development, statistical analysis, and validating digital biomarkers that support clinical research and drug development. The position collaborates with clinical, engineering, biostatistics, and product teams while ensuring reproducible, high-quality analytics and production-ready code.
KEY RESPONSIBILITIES
Wearable Data Processing
Develop Python pipelines for wearable sensor data processing.
Perform quality control (QC) on longitudinal time-series data.
Clean, preprocess, and normalize sensor data.
Detect and remove signal artifacts.
Handle missing data using appropriate imputation techniques.
Engineer clinically meaningful features from wearable data.
Signal Processing
Analyze accelerometry and actigraphy data.
Process heart rate variability (HRV) signals.
Analyze SpO₂ (oxygen saturation) signals.
Perform digital filtering and signal enhancement.
Conduct exploratory data analysis (EDA) on physiological signals.
Detect clinically relevant signal patterns.
Machine Learning & Model Development
Develop predictive models for longitudinal clinical data.
Build deep learning models using Transformer architectures.
Develop ensemble machine learning models.
Apply representation learning techniques.
Optimize machine learning algorithms.
Implement explainable AI (XAI) methods for model interpretation.
Statistical Analysis
Perform longitudinal statistical modeling.
Develop mixed-effects and hierarchical models.
Analyze repeated-measures clinical datasets.
Handle missing data appropriately.
Apply nested cross-validation techniques.
Perform leave-one-out (LOO) validation.
Conduct out-of-bag (OOB) evaluation where appropriate.
Algorithm Development
Develop algorithms for digital biomarkers.
Characterize physiological signals related to disease progression.
Identify clinically meaningful health metrics.
Support disease subtyping through data-driven approaches.
Validate analytical models using robust methodologies.
Software Development
Write clean, modular, production-quality Python code.
Design reusable object-oriented software components.
Build reproducible analytical pipelines.
Maintain version-controlled code using Git.
Participate in code reviews and pull request evaluations.
Debug and optimize data science workflows.
Collaboration & Cross-Functional Support
Collaborate with Clinical teams.
Work closely with Biostatistics teams.
Partner with Engineering and Product teams.
Coordinate with external analytics vendors.
Validate third-party analytical outputs.
Communicate technical findings to technical and non-technical stakeholders.
Research & Innovation
Develop digital biomarker methodologies.
Contribute to wearable analytics research.
Support digital health innovation initiatives.
Improve data science workflows and analytical methodologies.
Promote reproducible research practices.
Mentorship & Technical Leadership
Participate in technical code reviews.
Mentor junior data scientists.
Promote software engineering best practices.
Improve coding standards and development quality.
Support team knowledge sharing initiatives.
EDUCATIONAL QUALIFICATIONS
Preferred
PhD in:
Data Science
Biostatistics
Biomedical Engineering
Computer Science
Artificial Intelligence
Machine Learning
Related Quantitative Discipline
Accepted
Master's Degree in a related field with relevant experience.
EXPERIENCE REQUIREMENTS
Required
PhD: 3–5 years of relevant experience.
Master's: 6–9 years of relevant experience.
Experience in Digital Health, Pharmaceutical Industry, or Medical Devices.
Hands-on experience with wearable sensor data.
Strong production-level Python programming experience.
Experience in longitudinal statistical modeling.
Experience developing machine learning algorithms for healthcare applications.
Preferred
Experience with AWS cloud computing.
Experience with GPU-based model training.
Experience validating third-party analytics.
Experience with sleep analytics.
Experience with circadian rhythm modeling.
Experience with biomechanical or movement analytics.
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