Senior Manager RWE Biostats

At Bristol Myers Squibb, our employees often ask, "Who are you working for?"-a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

Position Summary

You will join a cutting-edge Drug Development Data Science and Advanced Analytics (DSAA) team to advance the global drug development process. We are looking for a candidate with strong computational, statistical, and data engineering capabilities and a demonstrated track record of working with real-world data (RWD), including electronic health records (EHR), claims data, patient registries, and other real-world evidence (RWE) sources, to generate actionable insights that inform clinical trial design and treatment evaluation. This role requires deep expertise across the full RWD analytics lifecycle: from data sourcing, engineering, and quality assessment, through to statistical analysis, summary extraction, and AI/ML predictive modeling.

In addition to the RWD focus, this role contributes to broader data science objectives spanning genomics, proteomics, imaging, flow cytometry, and other biomarker data types generated from clinical trials. As a hands-on individual contributor, you will drive exploratory and confirmatory analyses that support drug development decisions across early-to-late phase programs, collaborating closely with Biostatistics leads, Translational and Clinical Scientists, and cross-functional partners. We are looking for a hands-on, state-of-the-art practitioner.

What You'll Do

Real-World Data Science (Deep Expertise)

• Data Engineering & Infrastructure

• Design, build, and maintain scalable data pipelines for ingesting, harmonizing, and transforming large-scale RWD sources, including EHR, medical/pharmacy claims, patient registries, lab data, and linked multi-source datasets

• Develop and implement robust data quality frameworks to assess completeness, consistency, accuracy, and fitness-for-purpose of RWD sources for specific analytical questions

• Apply data standardization and interoperability best practices (e.g., OMOP CDM, FHIR, SNOMED, ICD, RxNorm) to enable cross-source analyses and longitudinal patient cohort construction

• Build reproducible, well-documented, version-controlled codebases using Python, R, SQL, and cloud platforms (e.g., AWS, Azure, Databricks)

• Data Processing, Curation & Cohort Development

• Define and implement rigorous patient identification, cohort selection, and exposure/outcome definition algorithms from complex, noisy real-world datasets

• Develop and apply algorithms for data cleaning, deduplication, record linkage, and handling of missing, irregular, or censored data in RWD contexts

• Extract clinically meaningful features and summary measures from unstructured and structured RWD, including NLP-based extraction from clinical notes and free-text fields

• Construct longitudinal patient-level datasets that accurately capture treatment patterns, disease progression, healthcare utilization, and outcomes

• Oncology Real-World Data (Emphasis Area)

• Work with oncology-specific RWD sources including EHR platforms (e.g., Flatiron Health, Tempus), tumor registries (e.g., SEER, NCDB), and molecularly-linked datasets integrating clinical outcomes with genomic profiling (e.g., NGS, TMB, MSI, PD-L1)

• Construct and validate oncology patient cohorts, including LOT sequences, biomarker-defined subgroups (e.g., PD-L1, MSI, TMB, EGFR, KRAS), and longitudinal treatment histories from fragmented, incomplete real-world records

• Apply methods appropriate for oncology RWD outcomes (rwOS, rwPFS, TTNT, rwRR) while addressing oncology-specific analytical challenges, including immortal time bias, informative censoring, death ascertainment, and treatment switching

• Support comparative effectiveness and external control arm (ECA) analyses for oncology programs, with awareness of FDA/EMA guidance on RWE use in oncology regulatory submissions

• Bring familiarity with immuno-oncology treatment landscapes and associated analytical complexities, including delayed response patterns and immune-related adverse events (irAEs)

• Statistical Analysis & Real-World Evidence Generation

• Apply advanced statistical and epidemiological methods appropriate for RWD, including propensity score methods (matching, weighting, stratification), instrumental variable analysis, difference-in-differences, interrupted time series, and other causal inference frameworks

• Perform robust characterization of patient populations, treatment patterns, comparative effectiveness, and outcomes from RWD to support clinical development strategy

• Develop and apply survival analysis and time-to-event models to evaluate treatment effects and disease trajectories in real-world cohorts

• Apply longitudinal and mixed-effects modeling approaches to repeated-measures RWD with appropriate handling of informative censoring and irregular observation times

• Contribute to the design and execution of RWE studies, observational analyses, and external control arm (ECA) analyses to inform regulatory submissions and clinical decisions

• AI/ML Predictive Modeling & Insight Generation

• Develop, validate, and deploy AI/ML predictive models using RWD to support patient stratification, treatment response prediction, disease progression modeling, and identification of novel prognostic and predictive factors

• Apply classical machine learning (e.g., regularized regression, gradient boosting, random forests) and deep learning approaches (e.g., recurrent/transformer architectures for longitudinal EHR data) with rigorous model evaluation and explainability practices

• Leverage NLP and large language model (LLM)-based approaches for structured and unstructured RWD extraction, phenotyping, and evidence synthesis

• Apply causal ML frameworks to estimate treatment effects and inform counterfactual analyses from observational RWD

• Implement strong evaluation standards: nested cross-validation, calibration assessment, out-of-sample validation, and transparent reporting of model performance and limitations

• Clinical Trial Design & Drug Development Informatics

• Leverage RWD analytics to characterize natural history of disease, estimate baseline event rates, and define estimands to inform clinical trial design, including feasibility assessments, site selection, and patient enrichment strategies

• Support development of external control arms (ECAs) and synthetic control analyses using RWD in collaboration with Biostatistics and Regulatory Affairs

• Contribute analytical insights to inform go/no-go decisions, dose selection, endpoint selection, and inclusion/exclusion criteria for clinical trials

• Partner with lead and protocol statisticians in contributing to statistical analysis plans (SAPs) for RWD/RWE analyses supporting drug development programs

Broader Multi-Modal Data Science (Clinical Trial & Drug Development)

• Develop and apply computational methods for patient segmentation and biomarker discovery from multimodal clinical and omics datasets in partnership with Translational, Clinical, and Statistical Scientists
• Execute data science and biomarker analyses on datasets from BMS clinical trials spanning genomics, proteomics, imaging, flow cytometry, and other high-dimensional biomarker data types
• Perform innovative statistical analyses of high-dimensional data (e.g., gene expression, sequencing, imaging features) generated by cutting-edge technologies
• Develop novel ways of integrating, mining, and visualizing, high-dimensional, and disparate data types, including integration of RWD with clinical trial data to enrich evidence generation
• Formulate, implement, test, and validate predictive models and implement efficient automated processes for delivering modeling results at scale

Collaboration & Technical Contribution

• Collaborate with cross-functional teams including clinicians, translational medicine scientists, biostatisticians, data engineers, regulatory scientists, and IT/engineering professionals
• Contribute to team excellence via code reviews, technical mentorship, and raising the overall engineering and methodological rigor of the team
• Communicate analytical results clearly and effectively to both technical and non-technical stakeholders, with strong data presentation and visualization skills
• Manage and coordinate resources to produce quality deliverables within timelines for competing priorities
• Build and maintain strong working relationships across the organization

Key Requirements

• Ph.D. in a relevant quantitative field (e.g., Biostatistics, Epidemiology, Data Science, Computer Science, Computational Biology, Biomedical Informatics, or related field) and 1+ years of academic/industry experience; or Master's Degree in a relevant quantitative field and 3+ years of industry experience
• Deep, hands-on expertise in real-world data science, including end-to-end experience with RWD sources (EHR, claims, registries) across data engineering, quality assessment, cohort construction, statistical analysis, and AI/ML modeling
• Strong experience in applying statistical and causal inference methods appropriate for observational RWD (e.g., propensity score methods, survival analysis, longitudinal modeling, external control arms)
• Proficiency in Python, R, and SQL for data engineering and statistical/ML analysis; experience with cloud platforms (e.g., AWS, Azure, Databricks) and distributed data environments
• Experience with common RWD standards and ontologies (e.g., OMOP CDM, FHIR, ICD, SNOMED, RxNorm) is required
• Experience in developing and validating AI/ML predictive models on high-dimensional, longitudinal, and/or irregular real-world datasets
• Strong experience in biomarker or multi-modal data analysis with data generated from clinical trials or electronic health records, in application to pharma R&D
• Familiarity with clinical trial design, drug development processes, and the role of RWE in regulatory and clinical decision-making
• 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

Preferred Qualifications

• Experience with NLP and/or LLM-based approaches for clinical text extraction, phenotyping, or evidence synthesis from RWD is highly preferred
• Experience with causal ML and explainable AI applied to observational RWD is highly preferred
• Experience with external control arm (ECA) or synthetic control analyses for regulatory applications is highly preferred
• Experience with genomics, proteomics, imaging, flow cytometry, or immunobiology datasets from clinical trials is highly preferred
• Experience with Survival Analysis and time-to-event modeling is highly preferred
• Hands-on experience with oncology RWD platforms (e.g., Flatiron, Tempus, IQVIA Oncology, ConcertAI) and oncology-specific endpoints (rwOS, rwPFS, TTNT) is highly preferred
• Familiarity with biomarker-driven patient stratification in oncology and FDA oncology RWE guidance (e.g., RWE Framework, Project Optimus) is preferred
• Familiarity with regulatory guidance on RWE (e.g., FDA RWE Framework, EMA guidance) and its application to drug development and submissions is preferred
• Knowledge of molecular biology and understanding of disease pathways is preferred
• Experience with federated data analysis, privacy-preserving analytics, or de-identification frameworks for RWD is a plus
• Experience managing or integrating third-party RWD vendors, data providers, or analytics platforms (e.g., Flatiron, Optum, IQVIA, TriNetX) is a plus
• Experience with scalable compute and deployment patterns, including cloud-based data platforms and parallelization for large-scale data processing and model training

We hire for skills and capabilities, not just credentials - if this role excites you, but doesn't perfectly match your resume, we encourage you to apply anyway.

Compensation Overview:

Brisbane- CA - US: $188,730 - $228,701Cambridge Crossing: $188,730 - $228,701Princeton- NJ - US: $164,110 - $198,862Seattle- WA: $180,520 - $218,751

The starting pay range(s) listed above is for full-time employees (FTE). You may also be eligible for additional discretionary incentive cash and stock opportunities. We determine starting pay thoughtfully - carefully considering the nature of the role, required skills, work location, schedule and the knowledge and experience you bring. Final compensation is guided by pay equity principles and applicable employment laws. Compensation programs are reviewed on an ongoing basis and may be adjusted over time to reflect evolving market factors, and individual, team or Company performance.

Benefits:

Subject to the terms and conditions of the applicable plans then in effect, you may be eligible to participate in our comprehensive benefit plans - including wellbeing support, retirement and financial protection benefits, and insurance offerings (medical, dental, vision, life and disability).

U.S.-based exempt employees are eligible for Flexible Time Off (FTO), which provides paid time off without a set accrual limit, subject to manager approval, along with 11 paid company holidays each year.

Non-exempt employees, RayzeBio employees, and employees located in Puerto Rico receive 160 hours of paid vacation annually for new hires (subject to manager approval), 11 paid company holidays, and 3 optional holidays.

Depending on eligibility, employees may also have access to additional time-off benefits, including paid sick leave, up to two paid volunteer days per year, summer hours flexibility, and leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs. Eligible employees also enjoy an annual Global Shutdown between Christmas Day and New Year's Day.

U.S.-based job seekers can explore full benefit offerings at https://careers.bms.com/benefits

How We Work

Where you work matters - because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities. Learn more at https://careers.bms.com/ways-of-working.

Supporting People with Disabilities

BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to adastaffingsupport@bms.com. Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity statement.

Candidate Rights

BMS will consider qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.

For roles based in Los Angeles County only: If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information: https://careers.bms.com/california-residents/

Data Protection

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If this posting is missing required information required by local law or incorrect, contact BMS at TAEnablement@bms.comwith the Job Title and Requisition number. Do not send application-related inquiries to this email. To check your application status, please login to your Candidate Home Account.

R1606428 : Senior Manager RWE Biostats