Senior Applied Biostatistician (R)

BioRender Inc.•Remote (Canada),

11d•Remote

About The Position

As the statistical expert at BioRender, you'll partner with the Graphing team to build statistical analysis and visualization capabilities scientists trust. You'll own the rigor of our stats service end-to-end — from algorithm choice and R implementation to the in-product UX that helps users apply the right analysis to their data. This is an opportunity to shape how millions of scientists analyze, understand, and communicate data by bringing rigorous statistical analysis directly into the products and workflows they rely on.

Requirements

Master's or PhD in Biostatistics, Statistics, or a closely related quantitative field
4+ years of experience post-academia in an analytical or applied statistics role, ideally working closely with product, engineering, and design teams
Production-level R proficiency, including package development, testing, and code review
Track record of writing your own code (R, Python, or similar) to implement complex statistical analyses — not just configuring existing tools or running canned procedures
Familiarity with common scientific analysis tools (e.g. GraphPad Prism, SAS, JMP, Stata) and their statistical conventions
Strong written and verbal communication skills, with the ability to explain statistical tradeoffs to non-specialists
Quick learner who stays current with new methodologies and tooling

Nice To Haves

Experience with multivariate analyses (e.g., principal component analysis, multiple linear regression), multi-way ANOVA, or pharma/biotech workflows like PK/PD modeling
Teaching or research experience in biology-related fields

Responsibilities

Own the statistical rigor of the Graphing product by validating analysis correctness, refining input/output accuracy, and ensuring our statistical workflows meet or exceed trusted scientific standards such as GraphPad Prism wherever appropriate.
Define the "recommended" analysis and sub-analyses for each user workflow, including the in-product context, explanations, and warnings that help users understand why a given method fits their data — and when it doesn't.
Partner with product and engineering to shape the stats roadmap: which methods to add next, how they interrelate, what complexity and infrastructure each new R model requires, and how to bring it into the system as a usable analysis and visualization.
Build and maintain production R code that powers these statistical features, with robust testing fixtures and QA workflows.
Level up the team's statistical literacy over time — bring product, engineering, and design along for the ride so SME knowledge compounds rather than staying siloed.
Be the diagnostic backstop for support escalations from Sales and Customer Success triage whether an issue is a stats service bug, a product app bug, or a user setup problem, and feed insights back into the roadmap.
Leverage modern AI-assisted tooling pragmatically to accelerate implementation and iteration where appropriate.
Identify opportunities where AI can unlock new workflows, automation, or intelligence, and partner closely with engineering and design to bring them to life responsibly and pragmatically.
Contribute to the broader BioRender Data Science brand and strategy.