Amazon Data Scientist EU AVS/VX BIE team 

United Kingdom, England, London 

432705578

Key job responsibilities
This role will sit in a data and analytics (DNA) team that supports two large program teams (EU AVS and VX), directly working with 15+ product managers. As a key member of the DNA team, you will play a crucial role in leveraging advanced analytics, machine learning, and AI to solve complex business problems and enhance vendor experiences. Specifically, you will lead the development of machine learning and AI solutions that will empower our global Voice of Vendor program to identify business areas (Listing, Ordering, Payments) that have biggest impact on the overall vendor satisfaction. You will provide meaningful vendor sentiment & anecdotes summaries to all partner teams using LLMs. You will develop models to identify the drivers of poor vendor feedback and we can take corrective and proactive actions to avoid this in the future. Every solution you will build will need to be production ready. Working closely with both PMs and PM-Ts, you will be involved in the end-to-end product feature development process from conception to productionisation.

- At least a Masters degree in computer science, physics, statistics, mathematics or a similar quantitative discipline.
- 5+ years of experience in using multiple data science methodologies to solve complex business problems.
- Experience as a data scientist, applied scientist, research scientist or equivalent role.
- Experience with theory and practice of data science, machine learning and artificial intelligence.
- Experience with data scripting languages (e.g. SQL, Python, R etc.) and statistical/mathematical software (e.g. R, SAS, or Matlab).
- Expertise in SQL, data modeling, warehousing, and building ETL pipelines.
- Experience with AWS technologies (e.g. Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions).
- Knowledge of software engineering best practices and version control systems.
- Excellent ability to communicate with technical and nontechnical stakeholders alike in written documents and verbal communication to collect data requirements.

- Hands-on experience in Natural Language Processing (NLP) and leveraging Large Language Models (LLMs) for real-world NLP applications.
- Experience in working on the entire lifecycle of a data product, from conceptualization to productionisation.
- E-commerce experience.