Senior Manager
Bristol Myers Squibb
6 days ago
Remote friendly (Lawrence, NJ)
United States
Other
Position Summary
Hands-on individual contributor on Drug Development Data Science and Advanced Analytics (DSAA) to advance global drug development by translating complex multi-modal data into testable hypotheses and actionable insights.
What You’ll Do
- Build and maintain Python pipelines for wearable/sensor time-series data (QC, preprocessing, artifact removal, imputation, feature engineering).
- Develop and validate models for longitudinal sensor data (time/frequency representations, digital filtering, representation learning, deep learning such as Transformers/ensembles) with explainability.
- Apply rigorous longitudinal/repeated-measures statistics (mixed-effects/hierarchical models; handle within-subject dynamics and missingness).
- Quantify clinically meaningful physiological measures (e.g., accelerometry/actigraphy, HRV, SpO₂) for disease progression/patient subtyping.
- Perform QC/validation of third-party and vendor digital biomarker outputs; ensure reproducible evaluation (nested CV, LOO, OOB, structured codebases, version control).
- Execute multi-modal biomarker discovery/segmentation across genomics, proteomics, imaging, flow cytometry, and other clinical trial/real-world datasets; contribute to SAPs.
- Communicate results to technical/non-technical stakeholders; collaborate cross-functionally; manage deliverables.
Key Requirements
- Ph.D. (or M.S. with 3+ years industry) in a relevant quantitative field.
- Deep expertise in digital health data science for wearable/sensor time-series; strong Python for production-quality code (Git/version control, modular pipelines).
- Experience with biomarker/multi-modal analysis from clinical trials/EHR; modeling for longitudinal/time-to-event outcomes.
- Familiarity with clinical trial design/biomarkers; proficiency in Python/R/SQL and cloud platforms; strong communication and collaboration.
Preferred Qualifications
- Experience with genomics/proteomics/imaging/flow cytometry/immunobiology; NLP; survival analysis/time-to-event; causal ML/explainable AI; molecular biology.
- Sleep analytics/circadian modeling; third-party analytics validation.
- Scalable compute/deployment (e.g., AWS multi-GPU).
Compensation Overview
- Princeton, NJ, US: $164,110–$198,862 (plus possible incentive cash/stock based on eligibility).
Benefits (highlights)
- Health coverage; wellbeing support; 401(k) and insurance/disability offerings.
- Paid time off (flexible time off or annual vacation/holidays depending on location/role type).
Hands-on individual contributor on Drug Development Data Science and Advanced Analytics (DSAA) to advance global drug development by translating complex multi-modal data into testable hypotheses and actionable insights.
What You’ll Do
- Build and maintain Python pipelines for wearable/sensor time-series data (QC, preprocessing, artifact removal, imputation, feature engineering).
- Develop and validate models for longitudinal sensor data (time/frequency representations, digital filtering, representation learning, deep learning such as Transformers/ensembles) with explainability.
- Apply rigorous longitudinal/repeated-measures statistics (mixed-effects/hierarchical models; handle within-subject dynamics and missingness).
- Quantify clinically meaningful physiological measures (e.g., accelerometry/actigraphy, HRV, SpO₂) for disease progression/patient subtyping.
- Perform QC/validation of third-party and vendor digital biomarker outputs; ensure reproducible evaluation (nested CV, LOO, OOB, structured codebases, version control).
- Execute multi-modal biomarker discovery/segmentation across genomics, proteomics, imaging, flow cytometry, and other clinical trial/real-world datasets; contribute to SAPs.
- Communicate results to technical/non-technical stakeholders; collaborate cross-functionally; manage deliverables.
Key Requirements
- Ph.D. (or M.S. with 3+ years industry) in a relevant quantitative field.
- Deep expertise in digital health data science for wearable/sensor time-series; strong Python for production-quality code (Git/version control, modular pipelines).
- Experience with biomarker/multi-modal analysis from clinical trials/EHR; modeling for longitudinal/time-to-event outcomes.
- Familiarity with clinical trial design/biomarkers; proficiency in Python/R/SQL and cloud platforms; strong communication and collaboration.
Preferred Qualifications
- Experience with genomics/proteomics/imaging/flow cytometry/immunobiology; NLP; survival analysis/time-to-event; causal ML/explainable AI; molecular biology.
- Sleep analytics/circadian modeling; third-party analytics validation.
- Scalable compute/deployment (e.g., AWS multi-GPU).
Compensation Overview
- Princeton, NJ, US: $164,110–$198,862 (plus possible incentive cash/stock based on eligibility).
Benefits (highlights)
- Health coverage; wellbeing support; 401(k) and insurance/disability offerings.
- Paid time off (flexible time off or annual vacation/holidays depending on location/role type).