Responsibilities - Translate / Interpret: Partner with cross-functional teams to translate business questions into rigorous causal inference problems - Design observational studies and quasi-experiments to measure marketing effectiveness when traditional A/B tests are infeasible - Work with data engineering to instrument new data pipelines when existing data cannot answer the causal question - Measure / Quantify / Expand: Own and evolve production attribution models across multiple marketing channels, with the reliability, latency, and reproducibility that automated downstream consumers depend on - Build and maintain causal inference pipelines using methods such as Difference-in-Differences, Synthetic Control, Double Machine Learning, and Media Mix Models - Develop and maintain calibration systems that benchmark model outputs against RCTs — owning the measurement truth layer - Write scalable, modular, SDE-standard PySpark/Python codebases (CI/CD, test isolation, structured logging) that process large-scale event data and deploy to production with confidence - Continuously improve model accuracy through feature engineering, heterogeneity analysis, and sensitivity testing - Explore / Enlighten: Investigate anomalies in model outputs and deep-dive to identify root causes - Research and prototype next-generation measurement methods and apply AI/LLM-based tooling to accelerate the science development lifecycle - Make Decisions / Recommendations: Present findings to senior leadership with clear recommendations - Build dashboards, agent-consumable APIs, and self-service tools that let stakeholders (and downstream systems) explore results independently - Write production-quality Python for data analysis, model training, calibration, and result publishing Requirements - 3+ years of building models for business application experience - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - Experience programming in Java, C++, Python or related language - Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing Nice to Have - Experience in professional software development - Experience in designing experiments and statistical analysis of results Benefits - Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage) - 401(k) matching - paid time off - parantal leave access - benefits overview at https://amazon.jobs/en/benefits Compensation - USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually - Your Amazon package will include sign-on payments and restricted stock units (RSUs) - Final compensation will be determined based on factors including experience, qualifications, and location How to Apply - For accommodations during the application and hiring process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. About Company - Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. - Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. - It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. - Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. Other