Job description
Join our rapidly growing analytics team as a Senior Statistician in San Francisco. You will design and execute rigorous statistical analyses that drive strategic decisions across product, marketing, and operations. This role combines methodological depth with practical application, offering a platform to influence high-impact initiatives.
At Pinnacle Analytics Group, we value rigorous experimentation, transparent code, and cross-functional collaboration. You will own end-to-end analyses—from study design and data wrangling to model development and results communication.
What you’ll do includes building statistical models, performing hypothesis testing, conducting causal inference, and mentoring junior analysts. You’ll partner with data engineers to ensure data quality and reproducibility, and translate complex findings into actionable insights for stakeholders.
We offer a competitive salary, comprehensive benefits, equity, and a culture that values curiosity, integrity, and impact. This is a hybrid role with in-office collaboration in San Francisco.
Responsibility
- Lead design and analysis of experiments, observational studies, and quasi-experimental designs.
- Develop and validate statistical models to support product, marketing, and operational decisions.
- Collaborate with data engineers to ensure clean data pipelines, reproducible analyses, and robust monitoring.
- Interpret results, create data visualizations, and communicate findings to non-technical stakeholders.
- Mentor junior statisticians and analysts; review code and ensure best practices in statistics and reproducibility.
- Define and evaluate key metrics, perform power analysis, and manage multiple analyses end-to-end.
- Stay current with statistical methodologies and contribute to the evolution of our analytics toolkit.
- Assist in translating business questions into rigorous statistical analyses and actionable recommendations.
Qualification
- MS or PhD in Statistics, Biostatistics, Mathematics, or a closely related field.
- 5+ years of applied statistics, data science, or analytics experience in industry.
- Proficiency in R and Python; strong SQL querying skills; experience with data manipulation and modeling packages.
- Strong knowledge of experimental design, hypothesis testing, causal inference, and Bayesian methods is a plus.
- Experience building predictive models, risk scoring, or segmentation analyses; familiarity with ML techniques.
- Excellent communication skills with ability to explain complex concepts to non-technical audiences; demonstrated leadership or mentorship.
- Strong problem-solving, critical thinking, and attention to detail; ability to manage multiple priorities.
- Experience with data visualization tools (Tableau, Power BI) and version control (Git).