Data Scientist II, RufusX Science UK

Overview

In this Data Scientist role, you’ll advance AI-powered, multimodal shopping experiences for Rufus, Amazon’s AI-driven search assistant. You’ll shape how customers discover and compare products using NLP, ML, and data-driven experimentation at scale. You’ll work with cross-functional teams to measure and improve conversational systems, information retrieval, and recommender components. This is a high-impact opportunity to translate analytics into production features that elevate customer experience and conversion.

Responsibilities

• Analyze and model large multimodal datasets to derive insights for helping customers across the shopping journey
• Develop scalable solutions using statistics, machine learning, and data mining on structured and unstructured signals
• Design and analyze A/B tests and experiments to evaluate feature and model improvements
• Build metrics, dashboards, and reporting frameworks to monitor system performance and business impact
• Create predictive models and conduct deep-dive analyses to enhance customer experience and satisfaction
• Collaborate with Applied Scientists and Engineers to translate insights into production systems and deployment
• Automate large-scale data analysis, ETL pipelines, metric generation, and experimentation frameworks
• Communicate results to technical and non-technical audiences via presentations, reports, and data visualizations

Key requirements

• Experience in ML or data scientist roles at a large technology company
• Experience with data scripting languages (SQL, Python, R) or statistical software (R, SAS, Matlab)
• Strong ability to communicate complex concepts in written and spoken form
• Master’s degree or higher in Math, Statistics, Computer Science, or related field

• Effective written and verbal communication
• Cross-functional collaboration
• Ability to translate analytical insights into actionable actions
• Machine learning and statistical modeling
• Natural Language Processing (NLP) and GenAI
• Information retrieval, recommender systems, and knowledge graphs

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