Experience

Experience

Career path

My career has moved from collaborative academic biostatistics into industry-facing leadership roles centered on real-world evidence, applied analytics, and evidence generation at scale.

Across Datavant, Tempus AI, 23andMe, and earlier University of Kentucky roles, the common thread has been building high-quality statistical work that is scientifically rigorous, operationally useful, and relevant to real healthcare decisions, especially in contexts that align with biopharma evidence generation and development. That includes nearly 11 years of full-time biostatistics work, including the years I worked full time while also completing my PhD.

2026-present

Senior Director, Biostatistics

Datavant (formerly Aetion)

I lead the biostatistics and statistical programming functions within Datavant's Science organization. My work focuses on building best practices for regulatory-grade real-world evidence studies, developing internal tools like R packages and Shiny applications, and partnering with product teams on future opportunities, including thoughtful uses of AI. It is a role that combines scientific quality, team leadership, and operational execution.

September 2023-2026

Lead Real World Evidence Biostatistician, Medical Affairs

Tempus AI

At Tempus AI, I collaborated with external KOLs on public-facing, peer-reviewed genomic oncology research using real-world clinical and molecular data. I acted as a statistical lead for Medical Affairs by reviewing study proposals, developing statistical analysis plans, refining best practices for cohort extraction and programming, and mentoring junior biostatisticians.

I also led biostatistical work within Clinical Development related to treatment response monitoring and minimal residual disease in advanced cancers, and developed AI tools, internal agents, and reusable packages to improve efficiency and standardize workflows.

June 2022-August 2023

Biostatistician, Health Data Analysis, Computational Biology

23andMe Therapeutics

At 23andMe, I worked as a biostatistical partner on cross-functional teams supporting preclinical therapeutic pipelines through observational study design, applied analyses, and methodologic work in bias correction, disease clustering, and causal inference.

I also supported ongoing oncology clinical trial programs through statistical input on analyses and sample size calculations while overseeing work performed by external CROs.

September 2021-June 2022

Assistant Professor, Biostatistics

University of Kentucky, College of Public Health

I collaborated on the design, analysis, and reporting of observational and randomized biomedical studies and supported the NIH-funded CCTS BERD core, with substantial work involving claims and electronic health record data.

January 2021-June 2022

Lead Biostatistician / Lead Biomedical Data Scientist

University of Kentucky, Biostatistics Consulting and Interdisciplinary Research Collaboration Lab (CIRCL)

I was a founding member of CIRCL, where I helped establish best practices for statistical consulting, programming, reporting, and project management while mentoring staff and leading collaborative research efforts.

May 2015-August 2021

Biostatistician

University of Kentucky, College of Medicine

I collaborated independently with departments across the College of Medicine on study design, sample selection, statistical analysis, interpretation, and dissemination for biomedical research across a wide range of clinical areas.

Training

PhD in Biostatistics

University of Kentucky

My doctoral training centered on dementia, genetics, observational analysis, and translational health science. It shaped the statistical rigor that still anchors how I approach evidence generation today.

Methods and Platforms

Technical range

Statistical Computing

Extensive experience in R, RStudio/Posit, R Markdown, ggplot2, R Shiny, GitHub/version control, SAS, LaTeX, and cluster computing environments including Slurm.

Real-World and Clinical Methods

Applied work in observational study design, causal inference, time-to-event analysis, competing risks, mixed models, generalized estimating equations, and clinical research support.

Modern Analytic Workflows

Experience building analytic pipelines, statistical packages, visualization tools, and reusable reporting workflows that help teams move from ad hoc analyses to more durable systems.

Genomics and Omics Data

Familiar with genomic and molecular analysis workflows including RNA-seq, NanoString, GWAS, gene-based inference, eQTL analyses, and related translational applications.