Job description
NovaStat Analytics is seeking a Senior Applied Statistician to lead advanced analyses and guide data-driven decision making for enterprise clients. You will translate complex statistical findings into clear, actionable insights for cross-functional teams.
As a senior member of the analytics practice, you will design robust experiments, build scalable predictive models, and ensure rigorous documentation and reproducibility across projects.
Responsibility
- Lead design, execution, and interpretation of statistical analyses for product, marketing, and business objectives.
- Develop and validate predictive models (regression, classification, time-series) and causal inference methods to inform strategy.
- Collaborate with data engineering to ensure data quality, reproducibility, and scalable analytics pipelines.
- Design, run, and analyze A/B tests; power analysis and experiment design expertise.
- Develop reproducible research workflows using Python/R, SQL, and version control; maintain documentation and code quality.
- Create clear data visualizations and narratives to communicate complex results to non-technical stakeholders.
- Mentor junior statisticians and analysts; contribute to hiring and training initiatives.
- Present insights to executives and cross-functional teams, influencing product and business decisions.
Qualification
- Master’s or PhD in Statistics, Biostatistics, Applied Mathematics, or a related field; or equivalent industry experience.
- 5+ years of applied statistics or data science experience with real-world modeling and decision support.
- Proficiency in Python or R, SQL; experience with libraries such as scikit-learn/statsmodels; strong data wrangling skills.
- Expertise in time-series analysis, causal inference (A/B testing, uplift modeling), and Bayesian methods.
- Experience designing and analyzing experiments; strong statistical rigor and interpretation skills.
- Experience with data visualization and storytelling; ability to communicate complex results to non-technical stakeholders.
- Experience with cloud platforms (AWS or GCP) and production-grade analytics pipelines; familiarity with data governance and reproducible research practices.
- Strong collaboration, problem-solving, and project management abilities; attention to detail and drive for impact.