Job description
QuantBridge Analytics is seeking a Senior Applied Statistician to join our Boston team. You will apply advanced statistical methods to real-world data, design experiments, and deliver insights that drive strategic decisions across industries including finance, healthcare, and technology. This role blends rigorous analysis with practical storytelling to influence product and policy decisions.
We value curiosity, collaboration, and a hands-on approach to solving complex problems with robust, reproducible methods.
Responsibility
- Lead development, validation, and interpretation of statistical models (linear, generalized linear, mixed-effects, survival, and Bayesian frameworks) to inform business decisions.
- Design and analyze experiments and quasi-experimental studies (A/B/n tests, cohort studies) and translate results into actionable recommendations.
- Collaborate with data engineering and analytics teams to ensure high-quality data, reproducibility, and scalable workflows.
- Communicate complex statistical concepts and results to non-technical stakeholders through clear presentations and reports.
- Maintain rigorous documentation, develop reproducible analysis pipelines, and ensure code quality and version control.
- Mentor junior statisticians, contribute to methodological best practices, and participate in internal training and knowledge sharing.
- Develop and maintain analytics tools, dashboards, and automated reporting using R, Python, and SQL.
Qualification
- Master's or PhD in Statistics, Biostatistics, Mathematics, or a related field.
- 5+ years of applied statistics experience in industry or research settings.
- Expert proficiency in R and Python; experience with SAS is a plus.
- Strong background in experimental design, causal inference, and Bayesian methods.
- Solid data visualization and storytelling skills; ability to explain results to non-technical audiences.
- Experience with SQL and data wrangling; familiarity with cloud platforms (AWS, GCP) and workflow tools.
- Proven track record of reproducible research, code sharing, and collaboration across teams.