Design and develop the analytical engine that underpins a decision product, including how relationships are modeled and outputs are generated. Apply causal inference techniques across both experimental and non-experimental settings, including situations where randomized testing is not feasible, practical, or cost-effective. Leverage predictive modeling and optimization approaches to support decision-making and scenario analysis. Translate complex business questions into structured analytical frameworks and scalable solutions. Build production-quality analytical components that integrate into decision systems and can be deployed in partnership with engineering teams. Ensure model outputs are robust, interpretable, and appropriately reflect uncertainty and underlying assumptions. Partner cross-functionally with product, engineering, and business stakeholders to align the analytical engine with user needs and decision workflows. Continuously evaluate and improve modeling approaches to ensure accuracy, reliability, and business impact. Qualifications. Masters or PhD in a quantitative field such as statistics, economics, mathematics, data science, operations research, or a related discipline; or equivalent combination of education and relevant experience 5 years experience applying advanced analytical techniques, including causal inference, predictive modeling, and/or optimization 5 years experience using quasi-experimental or observational methods to evaluate business interventions in real-world settings. Proficiency in Python, R, or similar tools, with the ability to write clean, scalable, and production-ready code. Strong understanding of statistical modeling, inference, and data analysis. Ability to design analytical frameworks that support decision-making under uncertainty. Demonstrated ability to work effectively in cross-functional and ambiguous environments.