Senior Statistician at Loyal

Loyal is a clinical-stage veterinary medicine company developing longevity drugs for dogs. Our mission is to help dogs live longer, healthier lives. We’re pioneering a new category of veterinary prescription drugs that proactively and preventatively target the underlying drivers of aging, delaying the onset of disease and preserving quality of life as dogs age. We’ve already made significant progress in our mission to develop the first FDA-approved drugs to extend canine lifespan: as of early 2026, we’ve completed two of three major FDA review sections for our lead program, LOY-002, and are targeting submission of the third and final section later this year.

The Role

We’re seeking a Senior Statistician to join the Stats & Data Science team at Loyal, taking independent ownership of our preclinical statistical portfolio – study design and power analysis, aging biomarker analytics, efficacy, target discovery, and dose and mortality modeling. Your work will directly support drug development decisions and the scientific foundation behind Loyal’s path to FDA CVM approval for the first lifespan extension medicine for any species.

This role requires more than executing pre-specified analyses. You’ll need to think critically about the data itself – questioning whether an endpoint is well-defined, whether a proposed target holds up statistically, and exploring open-ended questions where the right method isn’t obvious. Omics is one such exploratory area we’re looking to build out, using differential abundance and pathway-level analysis to facilitate target and pathway discovery.

This is a role for someone senior enough to operate independently: leading preclinical statistical strategy and analysis end-to-end and serving as the statistical point of contact for R&D programs. You’ll partner closely with R&D scientists and the broader Stats & Data Science team, and will have the opportunity to grow into supporting clinical biostatistics work over time.

You will be reporting to our Statistics Manager, Research.

Your Daily Work Will Include

• Independently leading statistical design and analysis for preclinical studies: dose-response and mortality projection modeling, aging biomarker studies, enrollment/attrition and survival modeling
• Building and applying population PK/PD models (nonlinear mixed-effects, exposure-response relationships) to support internal dose-justification and formulation decisions, extending the team’s existing bioequivalence/crossover work
• Conducting exploratory analyses to support endpoint development and target/pathway discovery – thinking critically about what the data can and can’t support, not just executing pre-specified analysis plans
• Serving as statistical point of contact for preclinical studies and cross-functional R&D projects, coordinating directly with scientists and program leads
• Applying survival analysis, longitudinal/mixed-effects modeling, and simulation-based power/sample size methods across preclinical programs
• Creating and contributing to internal documentation and reproducible reporting (Quarto/Rmarkdown) for preclinical analyses
• Supporting clinical biostatistics: assisting with SAP development, sitting in on regulatory strategy discussions, and drafting analysis sections for review by the Statistics Manager
• Cross-functional relationships – partnering with Statisticians, Clinicians, and Scientists to support CVM/FDA-ready data strategies and analysis outputs.
• Mentoring junior statisticians on the team
• Peer-reviewing methodology from external consultants, manuscripts, and cross-functional deliverables for statistical rigor
• Identifying opportunities to improve statistical workflows, tooling, or methods across the preclinical portfolio

About You

• MS or PhD in Statistics, Biostatistics, Pharmacometrics, or a related field
• 8+ years of relevant statistical experience, with a demonstrated track record operating independently on a preclinical or translational statistics portfolio
• Hands-on population PK/PD modeling experience (WinNonLin, NONMEM, nlmixr2, Monolix, or equivalent) – nonlinear mixed-effects models, exposure-response analysis
• Demonstrated experience in experimental design, survival/longitudinal analysis, and post-hoc statistical analysis
• A track record of exploratory, hypothesis-generating analysis – not just confirmatory statistics – with comfort sitting with ambiguous data and pushing back on poorly defined endpoints or targets
• Strong attention to detail and ability to adhere to regulatory requirements
• Demonstrated ability to learn new statistical methods, tools, or scientific domains independently, without needing structured training
• Strong writing and communication skills, including to non-statistical audiences
• Fluency in R and/or Python; experience with Rmarkdown/Quarto or Jupyter for reproducible reporting
• Fluency in AI tools (e.g., Claude code)
• Nice to have: prior work in veterinary, animal health, or translational aging research; experience with data validation pipelines; exposure to regulatory-facing statistical writing (FDA/CVM/EMA); interest in growing into clinical trial biostatistics over time
• Excited about working at the forefront of a new category of veterinary medicine