Senior Manager, Data Scientist
$170,470–$206,567 year
On-sitePrinceton, New Jersey, United States or Brisbane, California, United States
Job Summary
Develop and apply novel computational methods for patient segmentation from multimodal clinical and omics datasets in partnership with Translational, Clinical, and Statistical Scientists. Execute biomarker analyses on datasets from BMS clinical trials and real-world data cohorts, while formulating, implementing, and validating predictive models to drive the biomarker strategy for clinical drug development. Collaborate with cross-functional teams including clinicians, statisticians, and IT professionals to optimize and validate biomarker assays for clinical trial usage. Manage resources to produce quality deliverables within timelines for competing priorities while supporting the scientific and statistical strategy of drug development. This role requires physical presence at the BMS worksite and offers a competitive salary ranging from $148,230 to $206,567 depending on location.
Required Qualifications
- Ph.D. in a relevant quantitative field (i.e. Computational Biology, Biostatistics, Statistics, Computer Science, etc.) and 1+ years of academic/industry experience
- Master's Degree in a relevant quantitative field and 3+ years of industry experience
- Strong experience in the analysis of data generated by one or more -omics or molecular assays
- Knowledge of molecular biology
- Understanding of disease pathways
- Strong experience in biomarker data analysis with data generated from clinical trials, or electronic health records
- Experience in modeling methods particularly in their application to pharma R&D
- Experience in the application of AI/ML
- Proficient in SQL, Python, and R and cloud platforms
- Experience developing statistical and machine learning models on high dimensional and high throughput data for time to event data and longitudinal outcomes
- Perspective in leveraging innovative approaches to expedite drug development and address the complexities of emerging data
- Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects
- Strong problem-solving and collaboration skills, and rigorous and creative thinking
- Excellent communication, data presentation, and visualization skills
- Capable of establishing strong working relationships across the organization
- On-site Protocol Physical presence at the BMS worksite or physical presence in the field
Desired Qualifications
- 1+ years of academic/industry experience (if holding a Ph.D.)
- 3+ years of industry experience (if holding a Master's Degree)
- Experience with classical ML and Deep Learning algorithms
- Hands-on state-of-the-art practitioners
- Experience modeling multimodal (clinical, omics, real-world data)
- Experience writing, reviewing and executing protocols and statistical analysis plans (SAP) for biomarkers and diagnostics
- Experience executing biomarker analyses on datasets from BMS clinical trials and real-world data cohorts
- Experience performing relevant and innovative statistical analyses of high-dimensional (e.g. gene expression, sequencing) data generated by cutting edge technologies
- Experience developing and applying novel or existing computational methods for patient segmentation from multimodal clinical and omics datasets
- Experience developing predictive biomarker(s) and precision medicine
- Experience optimizing and validating biomarker assays for clinical trial usage
- Experience formulating, implementing, testing, and validating predictive models
- Experience implementing efficient automated processes for producing modeling results at scale
- Collaboration with cross-functional teams, including but not limited to clinicians, data scientists, translational medicine scientists, statisticians, and IT professionals
- Manage and coordinate resources to produce quality deliverables within timelines for competing priorities
- Ability to effectively lead and coach others
- Strong attention to detail
- Knowledge of molecular biology, understanding of disease pathways
- Experience developing statistical and machine learning models on high dimensional and high throughput data for time to event data and longitudinal outcomes
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