Head of Statistics

Novi Sci is a data science organization with a dual mission:
1) developing best-in-class software to support the generation of RWE and
2) executing high-impact, tech-enabled scientific consulting and research.

We are seeking a Head of Statistics who will own the statistical functional area- serving as the center of excellence for statistical rigor, methodology, methods standardization, and professional development across the organization. This is a hands‑on leadership role for a methodologist who wants to build: building a team, building methods, and building the statistical backbone of real‑world evidence software.

You will work shoulder‑to‑shoulder with the Head of Science, Product, Engineering, and Statistical Software Development, translating advanced causal inference and epidemiologic methods into robust software requirements, clear APIs, and scalable analytical tools. If you enjoy making hard statistical problems accessible, and care deeply about scientific credibility, this role is for you.

• In collaboration with the Head of Statistical Software Development, define and execute the statistical methods roadmap across products and services
• Serve as the functional center of excellence for statistical rigor, methods standardization, code reproducibility, and adoption of advanced causal inference and epidemiologic methods for real-world data
• Lead the development and implementation of causal inference, advanced epidemiologic, and statistical methods for real-world evidence generation, ensuring scientific rigor, transparency, and reproducibility
• Guide the design of longitudinal and observational study workflows, including uncertainty quantification and sensitivity analyses
• Represent Novi Sci’s statistical perspective with external partners, clients, collaborators, and at scientific venues as a subject‑matter expert
• Collaborate with Product, Statistical Software Development, and Engineering to:
• Translate statistical and methodological needs into product requirements and roadmaps
• Advise on high-level system and architecture decisions
• Co‑design API specifications (inputs/outputs, parameterization, defaults, error handling, diagnostics) and review PRDs/tech specs for statistical fidelity
• Provide reference implementations and simulation harnesses used as ground truth for verification; help design CI checks and validation datasets
• Establish documentation standards for methods notes, assumptions, and user‑facing guidance
• Serve as a senior statistical contributor on select research pods, providing high‑level expertise, validating analyses, and pioneering new methods
• Architect statistical designs for causal estimation of complex interventions under confounding, missingness, and dependent censoring
• Develop and review statistical analysis plans (SAPs), code, figures, and outputs for validity, reproducibility, and regulatory readiness
• Support quality and compliance processes in partnership with the Head of Operations, ensuring analysis validation, audit trails, and reproducibility standards are met

• Act as a senior statistical advisor to client teams (HEOR, Med Affairs, Epi); help mature their internal evidence pipelines and governance
• Evaluate statistical methodology choices for complex study designs, including distributed and federated analyses
• Guide clients on methods choices for regulatory submissions and HTA assessments
• Serve as functional manager for all statisticians: hire, mentor, train, and conduct performance reviews
• Develop and enforce standards and SOPs for statistical analysis, code reproducibility, and methods adoption across the statistics group
• Foster a rigorous, supportive, and learning-oriented culture within the statistics functional group
• Uplevel engineers and PMs on statistical, advanced epidemiologic methods, including causal inference; uplevel statisticians on software craft (versioning, testing, CI/CD, profiling)

• PhD (or equivalent) in Biostatistics, Statistics, Epidemiology, or a related quantitative field
• 4+ years of post‑doctoral experience spanning causal inference, advanced epidemiologic methods, statistical software…