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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist, Customer Experience and Business Trends - **Company:** Amazon.com, Inc. - **Location:** Arlington, VA, United States - **Experience:** Expert - **Salary:** $159,200.0 - $215,300.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Amazon Web Services, Cloud Database, Information Engineering, Python (Programming Language), Machine Learning, Language Modeling, SQL Databases, Scripting, Apache Spark - **Published:** September 9, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=c6cc8f4a06372619 ## About the Role We are looking for someone with exceptional business judgment and metric intuition, paired with deep hands-on modeling skills: a strong point of view on what to measure, and the technical ability to build the models that measure it at scale. You should be equally comfortable pressure-testing a model's assumptions and explaining its "so what" to a VP in two sentences., * Master's degree or above in a quantitative field, or PhD and 6+ years of data scientist experience * Experience applying statistical modeling and machine learning methods (e.g., regression, forecasting/time series, classification, clustering, causal inference) to business problems. * Proficiency with data scripting and modeling languages (SQL and Python or R) and building analyses on large, messy datasets. * Demonstrated experience defining metrics and KPIs from scratch and connecting them to business decisions. * Experience communicating analytical and modeling results to senior, non-technical business audiences in writing., * Familiarity with experimentation/A-B testing and with modern ML tooling and cloud data environments (e.g., AWS, Spark). * Experience mentoring scientists/analysts and providing technical leadership on modeling and metric-design decisions. * Strong background in retail, e-commerce, or CPG competitive and market-share analytics (share-of-wallet, category penetration, price/selection benchmarking). ## Description * Own the definition, methodology, and evolution of BEAM's competitiveness metrics (e.g., share-of-wallet, segment-level penetration, price/selection/fulfillment competitiveness), including the trade-offs behind each definition. * Design, build, and validate statistical and machine learning models - forecasting, causal inference, segmentation, and anomaly detection - that power those metrics and surface competitive shifts. * Pull together disparate third-party transaction-panel data and internal Amazon signals into coherent, reproducible modeling pipelines that leadership can trust for recurring reporting. * Translate ambiguous business questions ("are we losing ground in grocery?") into the right metric, the right model, the right cut of data, and a clear, defensible answer. * Productionize models and analyses so they run reliably as recurring mechanisms, partnering with data engineers to scale and automate. * Produce monthly flash reports and deep-dive narratives that turn models and metrics into decisions for CXBT and business-line leadership. * Set the standard for analytical and modeling rigor on the team - methodology reviews, documentation, and guardrails against misleading cuts. * Mentor and technically uplift other scientists and BIEs on the team - reviewing modeling approaches and raising the bar on rigor (e.g., partnering with our Data Scientist on the MatchIQ model to strengthen methodology and validation). About the team CXBT is a group of diverse functions dedicated to understanding, improving, and influencing customer experience globally, across all of Amazon. We are builders who develop products, services, and data-driven approaches that shape offerings for nearly every Amazon business and customer type - consumers, developers, sellers, brands, employees, investors, streamers, and gamers. We work backwards from customer needs, combine technical and non-technical methods, and track industry and business trends. Our roles span Product Managers, Data Scientists, Economists, Business Intelligence Engineers, Data Engine, Applied Scentist, and Product Manager. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [JavaScript? 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