Senior Manager
Bristol Myers Squibb
4 hours ago
Remote friendly (Princeton, NJ)
United States
Other
Responsibilities:
- Drive exploratory (hypothesis-generating and hypothesis-driven) data science for drug development and clinical study design.
- Build and maintain Python pipelines for wearable/sensor time-series data (QC, preprocessing, artifact removal, imputation, feature engineering).
- Develop/validate longitudinal models (time/frequency representations, 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 measures (e.g., accelerometry/actigraphy, HRV, SpO₂) tied to disease progression/subtyping.
- Perform multimodal biomarker discovery and patient segmentation across omics and clinical/digital health data; contribute to SAPs.
- Build predictive models for time-to-event and longitudinal outcomes; integrate, mine, and visualize high-dimensional datasets.
- Collaborate cross-functionally; conduct code reviews/mentorship and communicate results to technical/non-technical stakeholders.
Qualifications (Required):
- Ph.D. in a relevant quantitative field (or Master’s with 3+ years industry experience).
- Deep hands-on digital health data science (wearable/sensor time-series: QC, preprocessing, artifact handling, imputation, feature engineering; HRV, SpO₂).
- Strong Python; production-quality, testable, OOP code; modular pipelines; Git.
- Biomarker/multi-modal analysis experience (clinical trials/EHR data).
- Modeling/AI-ML experience; proficiency in Python/R/SQL and cloud.
Preferred:
- Experience with genomics/proteomics/imaging/flow/immunobiology datasets; NLP; survival analysis; causal ML/explainable AI; molecular biology.
Compensation & Benefits (as stated):
- Princeton, NJ: $164,110–$198,862.
- Health coverage; wellbeing support; 401(k), disability/life/other insurance; Paid Time Off (flexible time off / vacation per region).
- Drive exploratory (hypothesis-generating and hypothesis-driven) data science for drug development and clinical study design.
- Build and maintain Python pipelines for wearable/sensor time-series data (QC, preprocessing, artifact removal, imputation, feature engineering).
- Develop/validate longitudinal models (time/frequency representations, 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 measures (e.g., accelerometry/actigraphy, HRV, SpO₂) tied to disease progression/subtyping.
- Perform multimodal biomarker discovery and patient segmentation across omics and clinical/digital health data; contribute to SAPs.
- Build predictive models for time-to-event and longitudinal outcomes; integrate, mine, and visualize high-dimensional datasets.
- Collaborate cross-functionally; conduct code reviews/mentorship and communicate results to technical/non-technical stakeholders.
Qualifications (Required):
- Ph.D. in a relevant quantitative field (or Master’s with 3+ years industry experience).
- Deep hands-on digital health data science (wearable/sensor time-series: QC, preprocessing, artifact handling, imputation, feature engineering; HRV, SpO₂).
- Strong Python; production-quality, testable, OOP code; modular pipelines; Git.
- Biomarker/multi-modal analysis experience (clinical trials/EHR data).
- Modeling/AI-ML experience; proficiency in Python/R/SQL and cloud.
Preferred:
- Experience with genomics/proteomics/imaging/flow/immunobiology datasets; NLP; survival analysis; causal ML/explainable AI; molecular biology.
Compensation & Benefits (as stated):
- Princeton, NJ: $164,110–$198,862.
- Health coverage; wellbeing support; 401(k), disability/life/other insurance; Paid Time Off (flexible time off / vacation per region).