Data Scientist II (AI)

TL;DR

Data Scientist II (AI): Developing and optimizing multimodal conversational AI systems for the Rufus shopping assistant with an accent on machine learning, NLP, and statistical modeling. Focus on designing experiments, analyzing large-scale datasets, and deploying production-ready models to enhance customer shopping experiences.

Location: London, UK (Onsite)

Company

A global leader in e-commerce and cloud computing, driving innovation in AI-driven shopping experiences.

What you will do

• Perform hands-on analysis and modeling of large-scale multimodal datasets to derive actionable insights.
• Develop scalable solutions for measuring and optimizing shopping assistant systems using ML and statistical techniques.
• Design and analyze A/B tests to evaluate new features and model improvements.
• Build predictive models and conduct deep-dive analyses to improve customer conversion and satisfaction.
• Collaborate with scientists and engineers to translate analytical insights into production systems.
• Establish automated processes for data analysis, ETL pipelines, and experimentation frameworks.

Requirements

• Master's degree or higher in Math, Statistics, Computer Science, or a related field.
• Experience in a ML or data scientist role within a large technology company.
• Proficiency in data scripting languages such as Python, SQL, or R.
• Strong background in machine learning, statistical modeling, and data analysis techniques.
• Excellent written and verbal communication skills for technical and non-technical audiences.

Nice to have

• Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2.
• Experience in defining and creating benchmarks for assessing GenAI model performance.
• Experience working on multi-team, cross-disciplinary projects.

Culture & Benefits

• Inclusive and diverse work environment.
• Opportunity to work on large-scale, high-impact AI projects.
• Collaboration with a large, multidisciplinary team of scientists and engineers.
• Commitment to employee privacy and data security.
• Support for workplace accommodations and adjustments.