Data Scientist II, RufusX Science

We are looking for a passionate, talented, and inventive Data Scientist with a strong machine learning and analytics background to help build industry‑leading language technology powering Rufus, our AI‑driven search and shopping assistant, helping customers with their shopping tasks at every step of their shopping journey.

This innovative role focuses on developing conversation‑based, multimodal shopping experiences, utilizing data analysis, statistical modeling, machine learning (ML) technologies, and experimentation to drive product decisions and optimize customer experiences.

Our mission in conversational shopping is to make it easy for customers to find and discover the best products to meet their needs by helping with their product research, providing comparisons and recommendations, answering product questions, enabling shopping directly from images or videos, providing visual inspiration, and more. We do this by leveraging advanced analytics, Natural Language Processing (NLP), Machine Learning (ML), A/B testing, causal inference, and data‑driven insights to continuously improve our systems.

As a Data Scientist on our team, you will be responsible for the analysis, modeling, and optimization of AI technologies that will shape the future of shopping experiences. You will play a critical role in measuring and improving multimodal conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graphs, to be tailored to customer needs.

You will handle Amazon‑scale use cases with significant impact on our customers' experiences. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include designing experiments, analyzing results, and launching new features, products and systems.

You will:

• Perform hands‑on analysis and modeling of enormous multimodal datasets to develop insights into how to best help customers throughout their shopping journeys.
• Use statistical methods, machine learning, and data mining techniques to create scalable solutions for measuring and optimizing shopping assistant systems based on a rich set of structured and unstructured contextual signals.
• Design and analyze A/B tests and experiments to evaluate new features and model improvements, ensuring statistical rigor and actionable insights.
• Develop metrics, dashboards, and reporting frameworks to monitor system performance, customer engagement, and business impact.
• Build predictive models and conduct deep‑dive analyses to identify opportunities for improving customer experience, conversion, and satisfaction.
• Collaborate with Applied Scientists and Engineers to translate analytical insights into production systems, working closely on model evaluation and deployment.
• Establish automated processes for large‑scale data analysis, ETL pipelines, metric generation, and experimentation frameworks.
• Communicate results and insights to both technical and non‑technical audiences, including through presentations, written reports, and data visualizations.

The Rufus Features Science team, based in London, works alongside ~150 engineers, designers and product managers, shaping the future of AI‑driven shopping experiences  team works on every aspect of the Rufus AI, from making Rufus agentic, enabling customers to set price alerts or empower Rufus to act on their behalf and automatically purchase products when the price is right, to understanding multimodal user queries and generating answers that combine text, image, audio and video, including deep research reports that scour the web and the Amazon catalog to provide detailed and personalised shopping guidance.

We utilize and advance state‑of‑art techniques in the fields of Natural Language Processing, gen AI, Information Retrieval, Machine/Deep Learning, and Data Mining. We validate our work by actively participating in the internal and external scientific communities.

• Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance.
• Experienc…