Job description
NovaAnalytics is seeking a Senior Applied Statistician to join our growing Analytics team in Boston. In this role, you will lead statistical analyses, design experiments, and translate complex data into actionable business insights across healthcare, finance, and consumer analytics. The ideal candidate has deep expertise in statistical modeling, regression, time series, experimental design, and Bayesian methods, with proficiency in R and Python and experience in SQL-based data querying. You will work closely with data scientists, data engineers, and product stakeholders to drive data-driven decision making and ensure rigorous, reproducible results.
Responsibility
- Lead end-to-end statistical analyses for cross-functional projects, from problem framing and study design to interpretation and reporting.
- Develop and validate predictive models, with rigorous assessment of assumptions, performance, and uncertainty.
- Design and analyze experiments and quasi-experiments (A/B tests, randomized trials, quasi-experiments) to inform product decisions.
- Collaborate with data engineers to ensure data quality, reproducibility, and scalable analytics pipelines.
- Communicate results clearly to non-technical stakeholders through storytelling, reports, and visualizations.
- Mentor junior statisticians and promote best practices in statistical methodology and experimentation.
- Stay current with methodological advances and contribute to internal knowledge sharing and documentation.
Qualification
- Master's or PhD in Statistics, Biostatistics, Mathematics, or a closely related field.
- 5+ years of applied statistics experience in industry or research settings.
- Strong expertise in regression modeling, time series analysis, survival analysis, multilevel modeling, and Bayesian methods.
- Proficiency in R and Python; experience with SAS or SQL is highly desirable.
- Excellent communication skills with the ability to explain complex statistical concepts to non-technical audiences.
- Experience designing and analyzing randomized experiments and observational studies with attention to confounding and causal inference.
- Ability to manage multiple projects, meet deadlines, and work collaboratively in a fast-paced environment.