Job description
Join NovaStat Analytics as a Senior Applied Statistician and help shape data-driven decisions across finance, healthcare, and consumer analytics. This role blends rigorous statistical methodology with practical business impact in a fast-paced, collaborative environment.
You will design, implement, and validate statistical models, lead experimental design, and partner with cross-functional teams to translate complex results into clear, actionable insights for executives and product leaders.
We value curiosity, rigorous reasoning, and the ability to communicate complex ideas with clarity. If you are passionate about applying statistics to real-world problems and mentoring others, this is the role for you.
Responsibility
- Lead development and validation of predictive models using R and Python; deploy models to production and monitor performance.
- Design and analyze experiments, A/B tests, and quasi-experimental studies to inform product and strategy decisions.
- Collaborate with data engineers, product managers, and researchers to translate business questions into statistical frameworks.
- Ensure reproducible research through version control, documentation, and robust validation protocols.
- Communicate results with clear visuals and storytelling tailored to non-technical stakeholders.
- Mentor junior statisticians and contribute to statistical governance, best practices, and code reviews.
- Assess data quality, handle missing data appropriately, and implement robust data cleaning pipelines.
Qualification
- Master's or PhD in Statistics, Mathematics, Economics, Epidemiology, or a closely related field.
- 5+ years of applied statistics experience in industry or research settings.
- Strong proficiency in R; Python (pandas, numpy, scikit-learn) and SAS are highly desirable.
- Demonstrated expertise in experimental design, A/B testing, causal inference, and model validation.
- Proficiency in SQL and data visualization tools (Tableau, Power BI) or equivalent.
- Excellent communication skills with the ability to explain complex results to non-technical audiences.
- Experience with Bayesian methods and basic machine learning concepts is a plus.
- Self-motivated, collaborative, and able to manage multiple priorities in a fast-paced environment.