Job description
NovaStat Analytics is a leading data science and analytics firm focused on turning complex data into actionable business insights. Based in Boston, MA, we partner with clients across healthcare, finance, and technology to drive measurable outcomes.
We are seeking a Senior Applied Statistician to lead advanced modeling initiatives, design robust experiments, and translate statistical findings into strategic recommendations.
In this role you will collaborate with cross-functional teams, ensure rigorous methodologies, and mentor junior statisticians while advancing our data science practice.
Responsibility
- Lead the design and execution of experiments and observational studies, ensuring robust methodology and reproducibility.
- Develop and validate statistical models including linear, generalized linear, mixed effects, time series, and Bayesian frameworks.
- Implement forecasting, anomaly detection, and decision-support analytics for business stakeholders.
- Apply causal inference methods such as propensity score matching, instrumental variables, and DAGs to guide strategy.
- Communicate complex findings clearly to non-technical audiences, through dashboards, reports, and presentations.
- Mentor junior statisticians and collaborate with data engineers to ensure high quality data pipelines and reproducible workflows.
- Ensure compliance with data governance, privacy standards, and ethical use of analytics throughout projects.
Qualification
- Master's or PhD in Statistics, Biostatistics, Econometrics, or a closely related field; equivalent practical experience considered.
- 5+ years of applied statistics experience in industry or research; strong portfolio of modeling projects.
- Proficiency in R and Python; experience with Stan or PyMC for Bayesian modeling; SQL for data querying.
- Deep knowledge of experimental design, regression techniques, time series, multivariate analysis, and causal inference.
- Experience with data visualization tools and communicating results to stakeholders; strong storytelling ability.
- Experience with large datasets, reproducible research practices, version control (Git), and collaboration in cross-functional teams.
- Strong problem-solving, communication, and project management skills; ability to work independently and in teams.