EU INTech Partner Growth Experience(PGX) is seeking an Applied Scientist to lead the development of machine learning solutions for the EU Consumer Electronics business. In this role, you will push the boundaries of advanced ML techniques and collaborate closely with product and engineering teams to create innovative buying and forecasting solutions for the business. These new models will primarily benefit Smart Retail project that aims to revolutionize CPFR (Collaborative Planning, Forecasting, and Replenishment) Retail operations, driving automation, enhancing decision-making processes, and achieving scale across eligible categories such as PC, Home Entertainment or Wireless. Smart Retail solution is composed of an internal interface automating selection management mechanisms currently performed manually, followed by the creation of a vendor-facing interface on Vendor Central reducing time spent collecting required inputs. The project's key functionalities include (i) a Ranging model operating from category to product attributes level, pre-ASIN creation and when selection is substitutable, (ii) an advanced forecasting model designed for new selection and accounting cannibalization, (iii) ordering inputs optimization in line with for SCOT guideline compliance, and intelligent inventory management for sell-through tracking. Smart Retail success also depends on its integration with existing systems (SCOT) to minimize manual intervention and increase accuracy. Key job responsibilities * Design, develop, and deploy advanced machine learning models to address complex, real-world challenges at scale. * Build new forecasting and time-series models or enhance existing methods using scalable techniques. * Partner with cross-functional teams, including product managers and engineers, to identify impactful opportunities and deliver science-driven solutions. * Develop and optimize scalable ML solutions, ensuring seamless production integration and measurable impact on business metrics. * Continuously enhance model performance through retraining, parameter tuning, and architecture improvements using Amazon’s extensive data resources. * Lead initiatives, mentor junior scientists and engineers, and promote the adoption of ML methodologies across teams. * Stay abreast of advancements in ML research, contribute to top-tier publications, and actively engage with the scientific community.
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
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