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Niche digital-health specialization reduces applicants despite strong brand and metro location.
Domain-specific wearable, clinical, and longitudinal modeling skills limit cross-industry transferability.
Explicit degree-linked experience ranges and specialized sensor/ML skills enforce strict selection.
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Own end-to-end development of Python data pipelines and machine learning models for wearable time-series data (accelerometry, HRV, SpO₂).
Implement rigorous data quality control, preprocessing, artifact handling, imputation, and feature engineering for longitudinal sensor data.
Collaborate with internal clinical, stats, engineering teams and external partners on data validation, code reviews, and technical mentorship, emphasizing reproducible research practices.
Experience: PhD with 3-5 years or MS with 6-9 years in Data Science, Biostatistics, Biomedical Engineering, Computer Science or related field.
Strong hands-on expertise with time-series sensor data (QC, preprocessing, artifact handling, imputation) focused on accelerometry/actigraphy and HRV/SpO₂ signals.
Proficiency in Python coding with production-quality, testable, modular pipelines; experience with Git/version control and collaborative development.
Experience with longitudinal statistical modeling for repeated measures data.
Work Experience Required: Explicitly specified (PhD:3-5 yrs, MS:6-9 yrs)
Highly technical individual contributor comfortable debugging and shipping clean, object-oriented Python code for sensor data analytics.
Experience working at the intersection of digital health analytics and clinical development in regulated or pharma environments.
Proven ability to communicate complex analytical results to both technical and non-technical stakeholders and collaborate with diverse multidisciplinary teams.