Job description
We are seeking a Senior Applied Statistician to join our growing analytics team in Boston, MA. This role combines rigorous statistical methodology with practical data analysis to influence product strategy, clinical research programs, and operational decisions.
The ideal candidate will translate complex quantitative results into clear business insights, collaborate across data, product, and research teams, and uphold standards for reproducible research and transparent communication.
Responsibility
- Lead development, validation, and deployment of predictive and causal models using R, Python, or SAS.
- Design and analyze experiments (A/B tests, factorial designs, quasi-experiments) with rigorous statistical rigor.
- Apply Bayesian and frequentist methods to address complex inference problems and uncertainty quantification.
- Collaborate with data engineers, scientists, and product teams to translate results into actionable recommendations.
- Communicate findings to both technical and non-technical stakeholders through reports, dashboards, and visuals.
- Ensure reproducible research workflows, documentation, and version control for all analyses.
- Mentor junior statisticians and data scientists; contribute to code reviews and best practices.
- Manage multiple projects, balance timelines, and deliver high-quality insights under tight deadlines.
Qualification
- Master's or PhD in Statistics, Biostatistics, Mathematics, or a closely related field; PhD preferred for senior level.
- 5+ years of hands-on experience applying statistics in industry (healthcare, biotech, tech) or academia with industry collaborations.
- Proficiency in R; Python; SAS; SQL. Experience with Stan or PyMC for Bayesian modeling is a plus.
- Strong foundation in experimental design, hypothesis testing, regression analysis, and multivariate methods.
- Experience with data visualization (ggplot2, seaborn, Tableau) and clear, persuasive communication of results.
- Excellent problem-solving, written and verbal communication skills; ability to explain complex concepts simply.
- Experience working cross-functionally in fast-paced environments; project management and mentoring experience preferred.
- Familiarity with data governance, reproducible research practices, and version control (Git).