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Strong pharma brand, metro Hyderabad location, and a widely sought senior data science title.
Specialized digital-health, wearable-sensor and clinical validation expertise reduces cross-industry transferability.
Explicit multi-year experience ranges and specialized wearable-sensor modeling requirements make shortlisting strict.
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Own end-to-end development of Python pipelines handling wearable sensor time-series data including QC, preprocessing, artifact removal, imputation, feature engineering, and modeling.
Develop and validate longitudinal sensor data models applying statistical and deep learning methods to derive clinically relevant measures associated with disease progression or subtyping.
Collaborate extensively with internal clinical, statistical, engineering teams and external partners; engage in code reviews, technical mentorship and maintain reproducible, high-quality codebases.
PhD (preferred) with 3-5 years or MS with 6-9 years experience in Data Science, Biostatistics, Biomedical Engineering, Computer Science or related field.
Hands-on experience with time-series sensor data (accelerometry/actigraphy), HRV, SpO₂ including QC, preprocessing, artifact handling, imputation, feature engineering is mandatory.
Strong Python programming skills including clean, testable, object-oriented code and collaborative development practices (Git, code reviews).
Experience with longitudinal statistical modeling for repeated measures data and proven ability to communicate analytical outcomes clearly to both technical and non-technical stakeholders.
Experienced, hands-on data scientist skilled in complex analysis of longitudinal wearable sensor data with strong coding ability; comfortable owning pipeline development and production-ready code.
Familiar with advanced signal processing, statistical longitudinal modeling, and modern machine learning including deep learning with techniques for model explainability.
Able to operate effectively in multidisciplinary teams interfacing with clinical, engineering, and external analytic partners, balancing rigorous validation and reproducibility.