Head of Statistics

Overview: Headwater Science (formerly Novi Sci) is a data science and methods company specializing in principled, reproducible evidence generation for complex clinical and regulatory challenges. With deep expertise in comparative effectiveness, causal inference, healthcare utilization and expenditure research, and regulatory-grade analytical software, Headwater Science provides the methodological foundation that delivers reproducible analytic pipelines, novel epidemiologic and statistical methods, and regulatory-grade software validated to hold up under the most demanding scrutiny. The company works with life sciences organizations as a long-term scientific partner. Headwater Science is a Highlander Health company. Learn more at headwaterscience.com. Statistical leadership & strategy 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 Headwater’s statistical perspective with external partners, clients, collaborators, and at scientific venues as a subject-matter expert Product partnership (requirements, architecture, APIs)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 High-value scientific delivery 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 Thought partnership for clients 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 Team building & mentorship 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) Required background 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 development, and real-world data (claims, EHR, registries) Demonstrated track record of leading complex observational studies through publication and/or regulatory use Deep, hands-on expertise in causal inference and advanced epidemiologic methods for real-world data, including: Target trial emulation IP weighted estimators for confounding, missingness, and censoring Clone-censor-weighted estimation of dynamic treatment regimens Marginal structural models (MSMs), IPTW, and IPCW Uncertainty quantification and sensitivity analysis Proven ability to translate statistical and methodological requirements into product requirements, architecture trade-offs, and API designs in close partnership with engineering Strong experience with R, including package development, testing, and performance profiling Experience collaborating closely with software engineers in a production or near-production environment Evidence of thought leadership (publications, invited talks, open-source contributions, or methods papers) Nice-to-have background Experience designing statistical APIs or platforms used by external customers Familiarity with regulatory expectations for RWE (e.g., FDA, EMA guidance on real-world evidence) Experience with CMS/Medicaid data governance and multi-source linkage/tokenization Prior experience managing or mentoring senior statisticians or data scientists Experience working in a fast-paced, product-driven or startup environment Comfort with protocol/report generators and figure/table QA pipelines (e.g., Quarto/R Markdown) Fluency with AI/ML coding tools (e.g., Git Hub Copilot, Cursor) and comfort incorporating them into statistical programming and reproducible research workflows