{"id":2360,"date":"2026-07-03T19:41:27","date_gmt":"2026-07-03T19:41:27","guid":{"rendered":"https:\/\/coderseditor.com\/itjobs\/job\/data-scientist-ii-rufusx-science-uk\/"},"modified":"2026-07-03T19:41:27","modified_gmt":"2026-07-03T19:41:27","slug":"data-scientist-ii-rufusx-science-uk","status":"publish","type":"job_listing","link":"https:\/\/coderseditor.com\/itjobs\/job\/data-scientist-ii-rufusx-science-uk\/","title":{"rendered":"Data Scientist II, RufusX Science UK"},"content":{"rendered":"

Data Scientist II, RufusX Science UK Overview<\/h2>\n\n\n\n\n\n\n\n\n
Company Name<\/strong><\/td>\nAmazon<\/td>\n<\/tr>\n
Job Role<\/strong><\/td>\nData Scientist II, RufusX Science UK<\/td>\n<\/tr>\n
Qualifications<\/strong><\/td>\nBachelor’s<\/td>\n<\/tr>\n
Category<\/strong><\/td>\nIT Jobs<\/td>\n<\/tr>\n
Job Type<\/strong><\/td>\nFull Time<\/td>\n<\/tr>\n
Location<\/strong><\/td>\nLondon<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

This role is for a data scientist focused on the science behind Rufus, Amazon\u00e2\u0080\u0099s AI-powered shopping assistant. The work centers on building and improving language-driven and multimodal shopping experiences that help customers research products, compare options, get recommendations, ask product questions, shop from images or video, and receive visual inspiration and other forms of guided support throughout the shopping journey.<\/p>\n

The position calls for someone who combines strong machine learning knowledge with analytics depth and a passion for using data to shape product decisions. The team uses advanced analytics, natural language processing, machine learning, experimentation, and causal inference to improve conversational shopping systems and make it easier for customers to discover products that fit their needs.<\/p>\n

What you will do<\/h3>\n
    \n
  • Investigate very large multimodal datasets to understand how customers interact with the shopping assistant and where the experience can be improved.<\/li>\n
  • Use statistical techniques, machine learning, and data mining to build scalable methods for measuring and optimizing assistant performance using both structured and unstructured signals.<\/li>\n
  • Plan and analyze experiments and A\/B tests to evaluate new features and model changes, making sure the results are statistically sound and useful for decision-making.<\/li>\n
  • Create dashboards, metrics, and reporting systems that help teams monitor product performance, customer engagement, and business impact.<\/li>\n
  • Build predictive models and perform deep-dive analyses to identify opportunities to improve conversion, satisfaction, and the overall customer experience.<\/li>\n
  • Partner with Applied Scientists and Engineers to move insights into production systems, including model evaluation and deployment work.<\/li>\n
  • Develop automated pipelines and processes for large-scale data analysis, ETL, metric creation, and experimentation workflows.<\/li>\n
  • Share findings with both technical and non-technical audiences through presentations, written reports, and data visualizations.<\/li>\n
  • Contribute to the measurement and optimization of multimodal conversational systems, especially those using large language models, retrieval, recommender systems, and knowledge graphs.<\/li>\n
  • Work closely with scientists, engineers, and product partners in London and across other locations to design experiments, launch features, and improve systems at Amazon scale.<\/li>\n<\/ul>\n

    Team context<\/h3>\n

    You would join the Rufus Features Science team in London. The team works with roughly 150 engineers, designers, and product managers and helps shape the future of AI-driven shopping at Amazon. Their scope covers many parts of Rufus, including making the assistant more agentic so it can help with actions such as setting price alerts or purchasing items automatically when conditions are right, as well as improving the system\u00e2\u0080\u0099s ability to understand multimodal queries and generate answers that combine text, images, audio, and video.<\/p>\n

    The team also works on deeper research-style shopping guidance that draws on both the web and Amazon\u00e2\u0080\u0099s catalog to produce detailed, personalized recommendations. Their technical work spans natural language processing, generative AI, information retrieval, machine learning, deep learning, and data mining, and they validate their work by contributing to both internal and external scientific communities.<\/p>\n

    What you need<\/h3>\n
      \n
    • Experience using machine learning and statistical modeling methods for data analysis, including knowledge of the factors that affect model performance.<\/li>\n
    • Previous work in a machine learning or data scientist role at a large technology company.<\/li>\n
    • Practical experience with scripting or analysis languages such as SQL, Python, or R, or with statistical software such as R, SAS, or Matlab.<\/li>\n
    • Ability to communicate complex ideas effectively in writing and in conversation.<\/li>\n
    • A master\u00e2\u0080\u0099s degree or higher in mathematics, statistics, computer science, or a related scientific field.<\/li>\n
    • Experience with AWS services such as S3, Redshift, SageMaker, EMR, Kinesis, Lambda, and EC2 is desirable.<\/li>\n
    • Experience building benchmarks to measure generative AI model performance is desirable.<\/li>\n
    • Experience working on cross-team, cross-disciplinary initiatives is desirable.<\/li>\n<\/ul>\n

      Additional information<\/h3>\n

      Amazon says it is an equal opportunities employer and makes hiring decisions based on experience and skills. The company also emphasizes its commitment to diversity, privacy, and an inclusive workplace. If you need a workplace adjustment or accommodation during the hiring process, including support for interviews or onboarding, Amazon directs candidates to its accommodations information. The posting also links to Amazon\u00e2\u0080\u0099s privacy notice and other candidate resources.<\/p>\n\n

      Degree Requirement:<\/strong> Bachelor’s<\/p>\n

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