Job description
NovaStat Analytics is seeking a Senior Statistician to join our Applied Statistics team in Boston. This role emphasizes building robust statistical models, delivering data-driven insights, and partnering with cross-functional teams to drive strategic decisions across various industries.
What you will do is collaborate on study design, modeling, and interpretation; ensure reproducible analytics with well-documented code and governance; communicate complex findings to non-technical stakeholders; mentor junior staff; and help shape best practices in statistical methodology.
Responsibility
- Develop and validate statistical models for complex data sets across multiple domains, including health, technology, and finance.
- Collaborate with data scientists, engineers, and business stakeholders to translate requirements into rigorous statistical deliverables.
- Design experiments, perform power analyses, and conduct rigorous hypothesis testing.
- Implement and maintain reproducible analytics pipelines using R and Python; ensure code is version-controlled and well-documented.
- Interpret results, synthesize insights, and present actionable recommendations to non-technical audiences.
- Mentor junior statisticians and contribute to internal standards, governance, and methodological documentation.
- Stay current with methodological advances and contribute to cross-functional strategy discussions.
Qualification
- MS or PhD in Statistics, Biostatistics, Mathematics, or a related field.
- 4+ years of hands-on statistical modeling experience in industry or academia.
- Proficiency in R and Python; strong SQL skills; experience with SAS is a plus.
- Expertise in Bayesian statistics, experimental design, regression analysis, time-series, and model evaluation.
- Experience with data visualization tools (Tableau or Power BI) and communicating results to diverse audiences.
- Strong problem-solving, written and verbal communication, and ability to work cross-functionally across teams.
- Familiarity with Git and best practices for reproducible research.