Job description
StatNova Analytics seeks a Senior Applied Statistics Lead to shape the company’s statistical strategy and deliver high-impact insights for a diverse client portfolio.
You will collaborate with cross-functional teams to design rigorous experiments, develop predictive models, and translate complex results into actionable business recommendations. The role combines advanced analytics with practical business impact, requiring strong communication skills and a hands-on approach to problem solving.
We offer a competitive compensation package, flexible work arrangements, and opportunities to advance within a fast-growing analytics organization.
Responsibility
- Lead end-to-end development of statistical models and analytics solutions for diverse client problems.
- Design and analyze experiments, A/B tests, and observational studies with rigorous causal inference approaches.
- Collaborate with data engineers to ensure data quality, reproducibility, and scalable workflows.
- Interpret model outputs and communicate insights to stakeholders through clear visualizations and reports.
- Mentor junior statisticians and foster a culture of rigorous methodology and documentation.
- Stay current with statistical methods, experimental design, and industry best practices to drive innovation.
- Lead the selection, validation, and deployment of predictive models in production environments.
- Contribute to business development by shaping proposals and presenting analytical solutions to clients.
Qualification
- MS or PhD in Statistics, Biostatistics, Data Science, or a related quantitative field.
- 5+ years of applied statistics experience in industry, consulting, or academia.
- Strong proficiency in R and Python; experience with SQL and data visualization tools (Tableau, Power BI).
- Expertise in statistical modeling (linear/m generalized regression, survival analysis, time-series, Bayesian methods) and causal inference.
- Experience with experimental design, A/B testing, and hypothesis testing in real-world settings.
- Excellent communication skills with the ability to translate complex results for non-technical stakeholders.
- Ability to lead projects, manage timelines, and collaborate with cross-functional teams.
- Familiarity with tooling for reproducible research (Git, notebooks) and production model deployment is a plus.