Job description
Join our team as an Applied Statistician where you leverage rigorous statistical methods to drive product and business decisions. This role blends data science, experimental design, and stakeholder collaboration in a fast paced environment. You will work across product, marketing, and engineering teams to translate data into actionable insights and scalable solutions.
We seek a collaborator who thrives on complex problems, robust methodologies, and delivering impact through analysis, modeling, and storytelling with data.
Responsibility
- Design and implement statistical models and analyses to inform product and business decisions.
- Lead experimental design, power analysis, and A/B testing strategies to optimize metrics.
- Collaborate with data engineers and product teams to translate data into actionable insights.
- Develop and validate predictive models including regression, time series, and classification.
- Perform data cleaning, feature engineering, and data quality assessments.
- Communicate findings through clear visualizations and executive-ready summaries.
- Ensure reproducible research through well documented code, notebooks, and pipelines.
Qualification
- Master's or PhD in Statistics, Biostatistics, Mathematics, or a related field; MS with extensive experience acceptable.
- Proficiency in R and Python; strong knowledge of SQL for data extraction.
- Experience with SAS is a plus; familiarity with other tools is beneficial.
- Strong knowledge of experimental design, survey sampling, causal inference techniques.
- Experience with machine learning methods and time series forecasting.
- Excellent communication, collaboration, and ability to explain complex analyses to non-technical stakeholders.
- Experience with data visualization tools such as Tableau or Power BI is desirable.
- Strong coding discipline, version control, and reproducible research practices.