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Tier-1 brand and metro location increase applicants, but niche wearable/sensor expertise limits generalist competition.
Strong biomedical sensor, clinical study, and longitudinal modeling expertise reduces cross-industry transferability.
Explicit degree/experience bands and niche sensor, longitudinal modeling, and production Python requirements create stringent filters.
Lead end-to-end development and validation of analytics pipelines for wearable and sensor-derived longitudinal time-series data including preprocessing, artifact handling, imputation, feature engineering, and advanced modeling.
Write and maintain production-quality Python code for accelerometry/actigraphy, HRV, and SpO₂ data, ensuring reproducibility, rigorous validation, and code reviews.
Collaborate with cross-functional teams and external partners to deliver clinically meaningful models and maintain quality and validation of third-party analytics outputs.
PhD (preferred) or MS in Data Science, Biostatistics, Biomedical Engineering, Computer Science, or related field.
3-5 years post-PhD or 6-9 years post-MS experience working on digital health initiatives involving time-series sensor data in pharma or medical devices.
Strong hands-on Python programming skills with experience shipping clean, testable, modular code; experience in version control and code review.
Demonstrated experience with time-series sensor data processing (QC, artifact handling, imputation, feature engineering) including accelerometry/actigraphy, HRV, and/or SpO₂, plus longitudinal statistical modeling for repeated-measures data.
Highly technical, hands-on data scientist who codes daily and develops production pipelines for longitudinal wearable sensor data.
Experienced in both signal processing and advanced modeling (including deep learning and longitudinal statistical methods) with knowledge of physiological signals related to clinical endpoints.
Comfortable working in collaborative environments involving internal multidisciplinary teams and external vendors, emphasizing rigor in validation and reproducibility.