Senior Biostatistician

Verily

. Provides statistical and study design leadership across the product lifecycle, from feasibility and preclinical evaluation through clinical validation and post-market monitoring .

Posted 10/7/2026full-timeUnited StatesSenior💰 $130,000 - $196,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in statistical design and analysis across the product lifecycle, with a strong focus on clinical, biomedical, and health technology research. Proficient in programming with R and Python, and experienced in generating evidence from diverse healthcare data sources.

Highest-signal resume keywords

Statistical Design LeadershipClinical Study AnalysisProgramming in R and PythonExperience with FDA SubmissionsBayesian Methods and Adaptive Trial Designs

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Applicant Tracking System Keywords

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Hard Skills

Statistical MethodsObservational Study DesignInterventional Clinical Study AnalysisEvidence GenerationData Quality EvaluationCausal Inference MethodsReproducible Research WorkflowsStatistical Analysis PlansTechnical Report DevelopmentPublication-Ready Analyses

Soft Skills

CommunicationCollaborationRisk IdentificationProblem-Solving

Tools & Technologies

RPythonGitAI-Assisted Development Tools

Industry Keywords

BiostatisticsEpidemiologyHealth TechnologyMedical DevicePharmaceutical ResearchDigital HealthReal-World DataHealth-Regulated EnvironmentElectronic Health RecordsPatient-Reported Data

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Provides statistical and study design leadership across the product lifecycle, from feasibility and preclinical evaluation through clinical validation and post-market monitoring
Leads statistical design and analysis across the entire product lifecycle
Partners with medical, product, engineering, data science, regulatory, and operations teams to define study objectives, estimands, analysis strategies, and decision criteria
Analyzes clinical, experimental, observational, real-world, and operational datasets to generate interpretable evidence
Evaluates data quality, bias, missingness, generalizability, model performance, and other factors affecting study interpretation
Communicates study designs, assumptions, results, limitations, and recommendations through protocols, reports, presentations, dashboards, and cross-functional discussions
Identifies risks to scientific integrity, bias, generalizability, regulatory interpretation, and product decision-making, and recommends practical approaches balancing rigor, feasibility, and timelines

Requirements

What you’ll need

Master’s degree or higher in biostatistics, statistics, epidemiology, data science, bioinformatics, biomedical informatics, or a related quantitative field
5+ years of experience applying statistical methods to clinical, biomedical, health technology, medical device, pharmaceutical, diagnostics, or life sciences research
Demonstrated expertise in designing and analyzing observational studies, interventional clinical studies, and real-world data studies that support evidence generation or product decision-making
Experience programming in R and/or Python for analysis of complex life science, biomedical or healthcare datasets
Experience with a range of evidence generation including diagnostics, devices, digital health, or AI/ML-enabled clinical products
Experience working with products in a health-regulated environment (e.g. submissions involving the FDA or other health authorities)
Experience working with diverse healthcare data sources, including EHR, claims, registry, device-generated, sensor, imaging, laboratory, or patient-reported data
Hands-on experience generating evidence from digital product data as a primary data source, including handling missingness, measurement error, and analytic approaches specific to continuous digital measures
Experience developing study protocols, statistical analysis plans, technical reports, and publication-ready analyses
Experience with Bayesian methods, adaptive trial designs, or causal inference methods for observational/RWD studies
Experience using reproducible research workflows, version control systems such as Git, and modern coding tools, including AI-assisted development tools where appropriate
Qualified applicants must not require employer sponsored work authorization now or in the future for employment in the United States

Benefits

Comp & perks

Bonus
Equity
Benefits