Senior Biostatistician

United StatesRemoteFull-timesenior

BiostatisticianLaboratory & Life Sciences

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Key Responsibilities

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Requirements Summary

Partner with the data science team to develop reusable cohort definitions, standardized analytical code, and templated research outputs.

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BiostatisticianLaboratory & Life Sciences

We are seeking an experienced Senior Biostatistician to lead the design, analysis, and reporting of clinical research using real-world cardiovascular and cardiac device data. As the first biostatistician in the organization, you will take ownership of research projects from statistical planning through peer-reviewed publication, collaborating closely with clinical leaders and data science teams. You will apply advanced statistical methods to complex longitudinal, recurrent-event, and time-to-event data to generate reliable, reproducible evidence. Beyond clinical research, you will use your expertise to evaluate operational and business initiatives and inform strategic decisions. AI-assisted coding tools will be an integral part of your workflow, with a strong emphasis on validating outputs and protecting sensitive health information. This remote opportunity offers substantial autonomy, the ability to establish research standards and infrastructure, and potential to expand your leadership across research-related functions.

Clinical Research Design and Statistical Planning: Lead statistical analysis planning for retrospective and real-world evidence studies, including target trial emulation. Define study estimands, cohorts, exposure windows, and outcomes in collaboration with clinical investigators, ensuring alignment with established methodological standards.
Advanced Statistical Analysis: Analyze complex clinical data using appropriate methods for count and rate outcomes, recurrent events, time-to-event outcomes, competing risks, and clustered or repeated-measures data. Apply techniques such as Poisson and negative binomial regression, mixed-effects models, generalized estimating equations, and survival analysis methods.
Bias Control and Data Integrity: Link and validate data across multiple clinical tables, confirm denominators and follow-up periods, and address issues such as left truncation and immortal time bias. Apply propensity score methods, inverse probability weighting, multiple imputation, and sensitivity analyses to address confounding, missing data, and informative censoring.
Scientific Writing and Publication: Prepare and review statistical methods and results for abstracts and peer-reviewed manuscripts, following relevant reporting standards such as STROBE and RECORD. Respond to reviewer feedback, conduct additional analyses when needed, and develop clear, defensible methodological responses.
Operational and Business Analytics: Evaluate operational changes and business initiatives using methods such as interrupted time series, segmented regression, difference-in-differences, and statistical process control. Communicate effect estimates, confidence intervals, limitations, and implications to leadership in accessible language.
AI-Assisted Analytical Workflows: Use AI coding assistants regularly to write, review, debug, and document R and SQL code and analytical reports. Independently validate generated code and results, follow approved-tool requirements, and safeguard protected health information.
Research Infrastructure and Reproducibility: Partner with the data science team to develop reusable cohort definitions, standardized analytical code, and templated research outputs. Maintain an organized analytical queue and ensure published findings can be reproduced from raw data extracts through documented, scripted workflows.
Clinical Quality and Additional Research Support: Provide targeted statistical support for quality improvement, patient safety monitoring, and clinical operations. When requested, assist with institutional review board documentation and evaluate predictive models for discrimination, calibration, clinical usefulness, and external or temporal validation.

Requirements

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Education: PhD in biostatistics, statistics, or epidemiology, or a master's degree in one of these fields with at least 8 years of experience applying statistical methods to health data and independently owning analyses.
Clinical Research Experience: Demonstrated experience as a lead or methods statistician on peer-reviewed cardiovascular clinical research, ideally involving electrophysiology, heart failure, cardiac devices, or closely related fields. General healthcare analytics experience alone is insufficient.
Publication Record: A track record of contributing to peer-reviewed publications as a statistician, including experience responding to reviewer critiques and defending analytical decisions.
Advanced Statistical Expertise: Strong command of methods for relational clinical data, count and rate outcomes with exposure offsets, recurrent events, competing risks, survival analysis, clustered data, confounding control, missing data, and informative censoring.
Programming Proficiency: High proficiency in R across the analytical pipeline, including R Markdown or Quarto, and sufficient SQL skills to query, join, and validate relational clinical tables.
Reproducible Research Practices: Experience with Git, dependency management tools such as renv, scripted analytical pipelines, and literate reporting to ensure transparent and reproducible results.
Communication and Judgment: Ability to explain complex statistical methods, findings, and limitations to physicians, clinical researchers, and nontechnical stakeholders. Must be comfortable communicating when the available data cannot support a definitive conclusion.
AI-Assisted Analytics: Current, regular use of AI coding assistants in analytical work, supported by a consistent process for checking generated code and results and protecting patient information.
Business and Operational Insight: Interest in applying statistical methods to operational and business questions beyond traditional clinical research.
Preferred Qualifications: Experience with cardiac implantable electronic device (CIED), electrophysiology, or remote monitoring data, particularly transmission-level data; published research involving target trial emulation; and working knowledge of Python.
Certifications: PStat or GStat certification is considered an advantage.
Remote Work Requirements: Must reside and work exclusively within the contiguous United States and be legally eligible to work in the country. A dedicated, private home office and reliable high-speed internet connection are required. Employees must notify and obtain approval from IT before working from a location other than their registered address. Limited travel may be required.