AppliedScientist

## Accountabilities This role is responsible for translating complex commercial questions into rigorous analytical solutions, leveraging large-scale datasets, statistical modeling, and machine learning techniques to inform strategic decision-making across the business. Partner with commercial, finance, and executive teams to define and solve high-impact business problems through structured analytical approaches Design and execute causal inference studies, experiments, and quasi-experimental analyses (e.g., Diff-in-Diff, propensity modeling) to measure impact of pricing, promotions, and investments Build, maintain, and optimize large-scale data models and pipelines using SQL and modern data tooling (e.g., dbt, BigQuery) Develop predictive models for demand forecasting, market share, and commercial performance to support strategic planning Apply advanced analytics to evaluate pricing elasticity, promotional effectiveness, and category economics Leverage LLMs and AI systems to structure unstructured commercial data and build internal tools that improve data accessibility Collaborate with cross-functional stakeholders to ensure insights are translated into actionable business decisions Establish scalable, well-documented analytical frameworks and best practices for experimentation and modeling Requirements: This role requires a strong applied scientist with deep expertise in statistics, causal inference, and large-scale data analysis, combined with the ability to translate technical findings into business impact. 5+ years of experience in applied data science, analytics engineering, or quantitative modeling roles in industry Strong proficiency in SQL with experience working on large-scale datasets and production-grade data models Solid foundation in statistics, experimental design, and causal inference methodologies Strong programming skills in Python (pandas and related data science ecosystem) Experience building and deploying predictive models and analytical framework