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Senior Manager, Data Science

Bristol Myers Squibb
3+ years
Not Disclosed
Hyderabad
1 June 2, 2026
Job Description
Job Type: Full Time Education: B.Sc/M.Sc/M.Pharma/B.Pharma/Life Sciences Skills: ICD-10 CM, CPT, HCPCS Coding, ICH guidelines, ICSR Case Processing, Interpersonal Skill, Labelling Assessment, MedDRA Coding, Medical Billing

Senior Manager, Data Science

Company: Bristol Myers Squibb
Location: Hyderabad
Department: Digital Health – Data Science & Advanced Analytics
Level: Senior Manager
Job Type: Full-Time
Travel Requirement: Less than 10%


Role Summary

The Senior Manager, Data Science is a highly technical, hands-on individual contributor role focused on developing advanced analytics and machine learning solutions using wearable and sensor-generated health data. The position involves building end-to-end data science pipelines, from raw signal processing to validated predictive models, supporting digital health initiatives and clinical research programs.

The role requires extensive coding, model development, validation, and collaboration with clinical, statistical, engineering, and product teams.


Key Responsibilities

Wearable & Sensor Data Analytics

  • Build and maintain Python-based pipelines for wearable and sensor-derived time-series data.

  • Perform:

    • Data quality control (QC)

    • Signal preprocessing

    • Artifact detection and removal

    • Missing data imputation

    • Feature engineering

  • Analyze longitudinal physiological datasets including:

    • Accelerometry

    • Actigraphy

    • Heart Rate Variability (HRV)

    • Oxygen Saturation (SpO₂)

Signal Processing & Feature Development

  • Develop algorithms for:

    • Signal detection

    • Digital filtering

    • Frequency and time-frequency analysis

  • Create clinically meaningful biomarkers and physiological measures.

  • Characterize disease progression and patient subtypes using digital health data.

Machine Learning & AI Development

  • Develop and validate predictive models using:

    • Deep Learning

    • Transformer Architectures

    • Ensemble Models

    • Representation Learning Techniques

  • Apply explainable AI methodologies where appropriate.

  • Optimize model performance and reliability for clinical applications.

Statistical Analysis

  • Apply advanced longitudinal statistical techniques:

    • Mixed Effects Models

    • Hierarchical Models

    • Repeated Measures Analysis

  • Handle missing data and within-subject variability using robust statistical methods.

Validation & Reproducible Research

  • Implement rigorous model evaluation frameworks:

    • Nested Cross Validation

    • Leave-One-Out Validation (LOO)

    • Out-of-Bag (OOB) Validation

  • Develop reproducible workflows and maintain high-quality documentation.

  • Ensure code quality, scalability, and regulatory readiness.

Cross-Functional Collaboration

  • Partner with:

    • Clinical Teams

    • Biostatistics Teams

    • Data Engineering Teams

    • Product Teams

  • Review and validate outputs from external vendors and analytics partners.

  • Participate in technical discussions and scientific decision-making.

Technical Leadership

  • Conduct code reviews and promote software engineering best practices.

  • Mentor team members on analytics methodologies and coding standards.

  • Drive continuous improvement in data science processes and technical rigor.


Required Qualifications

Education

  • PhD (Preferred) or

  • Master's Degree with significant experience in:

    • Data Science

    • Biostatistics

    • Biomedical Engineering

    • Computer Science

    • Related Quantitative Discipline

Technical Skills

  • Strong Python programming expertise.

  • Experience building production-quality analytics pipelines.

  • Advanced machine learning and deep learning knowledge.

  • Time-series analytics and signal processing expertise.

  • Statistical modeling of longitudinal clinical data.

  • Experience working with wearable or digital health datasets.