Senior Manager
Bristol Myers Squibb
August 29, 2026
Remote friendly (Princeton, NJ)
United States
Other
Position Summary
Hands-on individual contributor on Drug Development Data Science & Advanced Analytics (DSAA) team to translate complex multi-modal data into testable hypotheses and actionable insights across digital health and clinical trial biomarker data.
Responsibilities
- Build/maintain Python pipelines for wearable/sensor time-series data (QC, preprocessing, artifact removal, imputation, feature engineering).
- Develop/validate longitudinal models (frequency/time-frequency reps, digital filtering, representation learning, deep learning such as Transformers/ensembles) with explainability.
- Apply rigorous stats for repeated-measures/longitudinal data (mixed-effects/hierarchical models; within-subject dynamics and missingness).
- Quantify physiological/clinical measures tied to disease progression/subtyping; validate third-party/vender digital biomarker outputs.
- Execute exploratory and hypothesis-driven analyses to support drug development and clinical study design; contribute predictive biomarker/precision medicine approaches.
- Develop methods for patient segmentation/biomarker discovery and predictive modeling across genomics, proteomics, imaging, flow cytometry, and other biomarker data; produce scalable automated outputs.
- Collaborate cross-functionally; communicate results; uphold reproducible research standards.
Qualifications
- Ph.D. in a relevant quantitative field with 1+ year experience, or Master’s with 3+ years industry experience.
- Strong digital health/wearable expertise; strong Python (production-quality, OOP, modular pipelines, Git).
- Biomarker/multi-modal analysis experience with clinical trial/EHR data.
- AI/ML experience; proficiency in Python, R, SQL, and cloud platforms.
- Experience with time-to-event/longitudinal modeling; familiarity with clinical trials/biomarkers in regulatory decisions.
Preferred
- Genomics/proteomics/imaging/flow cytometry/immunobiology; NLP; survival analysis; causal ML/explainable AI; molecular biology.
Compensation & Benefits (high level)
- $164,110–$198,862 (Princeton, NJ, US). Benefits include medical/dental/vision, wellbeing, and financial protection (e.g., 401(k)).
- Paid time off (flexible time off or annual vacation depending on location/role).
Application
- If you’re intrigued but don’t perfectly match, you’re encouraged to apply.
Hands-on individual contributor on Drug Development Data Science & Advanced Analytics (DSAA) team to translate complex multi-modal data into testable hypotheses and actionable insights across digital health and clinical trial biomarker data.
Responsibilities
- Build/maintain Python pipelines for wearable/sensor time-series data (QC, preprocessing, artifact removal, imputation, feature engineering).
- Develop/validate longitudinal models (frequency/time-frequency reps, digital filtering, representation learning, deep learning such as Transformers/ensembles) with explainability.
- Apply rigorous stats for repeated-measures/longitudinal data (mixed-effects/hierarchical models; within-subject dynamics and missingness).
- Quantify physiological/clinical measures tied to disease progression/subtyping; validate third-party/vender digital biomarker outputs.
- Execute exploratory and hypothesis-driven analyses to support drug development and clinical study design; contribute predictive biomarker/precision medicine approaches.
- Develop methods for patient segmentation/biomarker discovery and predictive modeling across genomics, proteomics, imaging, flow cytometry, and other biomarker data; produce scalable automated outputs.
- Collaborate cross-functionally; communicate results; uphold reproducible research standards.
Qualifications
- Ph.D. in a relevant quantitative field with 1+ year experience, or Master’s with 3+ years industry experience.
- Strong digital health/wearable expertise; strong Python (production-quality, OOP, modular pipelines, Git).
- Biomarker/multi-modal analysis experience with clinical trial/EHR data.
- AI/ML experience; proficiency in Python, R, SQL, and cloud platforms.
- Experience with time-to-event/longitudinal modeling; familiarity with clinical trials/biomarkers in regulatory decisions.
Preferred
- Genomics/proteomics/imaging/flow cytometry/immunobiology; NLP; survival analysis; causal ML/explainable AI; molecular biology.
Compensation & Benefits (high level)
- $164,110–$198,862 (Princeton, NJ, US). Benefits include medical/dental/vision, wellbeing, and financial protection (e.g., 401(k)).
- Paid time off (flexible time off or annual vacation depending on location/role).
Application
- If you’re intrigued but don’t perfectly match, you’re encouraged to apply.