Senior Manager, Data Science
Location: Hyderabad, Telangana, India
Job ID: R1601747
Company: Bristol Myers Squibb
Role Summary
The Senior Manager, Data Science will serve as a hands-on individual contributor within Digital Health, responsible for developing advanced analytics, machine learning models, and signal processing pipelines using wearable and sensor-derived longitudinal data. The role focuses on transforming raw physiological signals into validated clinical insights through rigorous data science, statistical modeling, and software engineering practices. This position requires extensive coding, model development, validation, and collaboration with cross-functional stakeholders.
Key Responsibilities
Data Science & Analytics Development
Design, develop, and maintain Python-based analytics pipelines for wearable and sensor-generated time-series data.
Perform data quality control (QC), preprocessing, artifact detection, and signal cleaning.
Develop feature engineering methodologies based on clinically relevant endpoints and biomarkers.
Conduct exploratory data analysis (EDA) and signal characterization for physiological datasets.
Analyze accelerometry, actigraphy, heart rate variability (HRV), and oxygen saturation (SpO₂) data.
Signal Processing & Digital Biomarker Development
Apply advanced signal processing techniques to wearable sensor data.
Develop methods for signal detection, filtering, and feature extraction.
Utilize frequency-domain and time-frequency analysis approaches.
Quantify physiological measures associated with disease progression, disease monitoring, and patient stratification.
Support development and validation of digital biomarkers and clinical endpoints.
Machine Learning & Artificial Intelligence
Build predictive and descriptive models using longitudinal sensor data.
Develop deep learning solutions using Transformers, ensemble methods, and representation learning techniques.
Apply explainable AI methodologies to improve model transparency and interpretability.
Optimize model performance and robustness through advanced validation strategies.
Translate machine learning outputs into clinically meaningful insights.
Statistical Modeling & Research
Implement longitudinal statistical models for repeated measures data.
Apply mixed-effects and hierarchical modeling approaches.
Address missing data challenges through appropriate imputation techniques.
Develop study-specific analytical approaches for within-subject variability and longitudinal monitoring.
Ensure statistical rigor in all analyses and model evaluations.
Software Engineering & Reproducibility
Write clean, scalable, and production-quality Python code.
Develop modular, reusable, and maintainable data science workflows.
Conduct code reviews and contribute to software engineering best practices.
Manage version control using Git and collaborative development processes.
Maintain reproducible research standards and well-documented codebases.
Validation & Quality Assurance
Design and implement robust model evaluation frameworks.
Apply Nested Cross-Validation (CV), Leave-One-Out (LOO), and Out-of-Bag (OOB) validation methods.
Validate internally developed algorithms and third-party vendor solutions.
Ensure analytical outputs meet scientific, technical, and regulatory standards.
Cross-Functional Collaboration
Partner with Clinical, Biostatistics, Engineering, Product, and Digital Health teams.
Collaborate with external vendors and analytics providers.
Review and validate third-party analytical outputs.
Present technical findings to both technical and non-technical stakeholders.
Support strategic Digital Health initiatives through data-driven insights.
Technical Leadership
Provide technical mentorship and guidance to team members.
Participate in peer reviews and code review processes.
Promote engineering excellence, innovation, and best practices.
Contribute to continuous improvement initiatives within Data Science and Digital Health functions.
Required Qualifications
Education
PhD (Preferred) or Master's Degree in:
Data Science
Biostatistics
Biomedical Engineering
Computer Science
Artificial Intelligence
Machine Learning
Related Quantitative Discipline
Experience
PhD with 3–5 years of relevant experience OR
Master's Degree with 6–9 years of relevant experience.
Experience within Digital Health, Pharmaceutical, Biotechnology, Medical Device, or Healthcare Analytics environments.
Hands-on experience working with wearable device and sensor-generated datasets.
Experience developing analytical and machine learning solutions for healthcare applications.
Uttar Pradesh :
Agra | Gajraula | Gautam buddha Nagar | Ghaziabad | Gorakhpur | Greater Noida | Jhansi | kanpur | Lucknow | Mathura | Noida | Park City | Prayagraj | Satyamev | Varanasi |Gujarat :
Ahmedabad | Ankleshwar | Baroda | Bharuch | Gandhinagar | Gujarat | Halol | Jhagadia | Mehsana | surat | Tarasadi | Vadodara | Vapi |Maharashtra :
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Delhi | Gangtok | New Delhi | PAN-India |Assam :
Dibrugarh | Diburghar | Guwahati |West Bengal :
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Hajipur | Patna |Kerala :
Kannur | Kochi | Malappuram | Thiruvananthapuram | Trivandrum |Pondicherry (Puducherry) :
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Remote, India | Siliguri |Illinois :
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Tuscaloosa |D.C :
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Wilmington |North Rhine Westphalia :
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Bavaria |Bavaria :
Bayreuth | Erlangen | Munich | Regensburg | Wurzburg |Brandenburg :
Berlin |Baden-Wurttemberg :
Biberach an der Riß | Freiburg | Grenzach | Heidelberg | Karlsruhe | Konstanz | Stuttgart | Tubingen | Ulm |Hesse :
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Germany | GErmany |Lower Saxony :
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Greece |North Island :
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Manila |Croatia :
Croatia |Zagreb :
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