Data Scientist, AWS Quick Data

Amazon Quick Suite is an enterprise AI platform that transforms how organizations work with their data and knowledge. Combining generative AI-powered search, deep research capabilities, intelligent agents and automations, and comprehensive business intelligence, Quick Suite serves tens of thousands of users. Our platform processes thousands of queries monthly, helping teams make faster, data-driven decisions while maintaining enterprise-grade security and governance. From natural language interactions with complex datasets to automated workflows and custom AI agents, Quick Suite is redefining workplace productivity at unprecedented scale.

We are seeking a Data Scientist II to join our Quick Data team, focusing on evaluation and benchmarking data development for Quick Suite features. Our mission is to engineer high-quality datasets that are essential to the success of Amazon Quick Suite. From human evaluations and Responsible AI safeguards to Retrieval-Augmented Generation and beyond, our work ensures that Generative AI is enterprise-ready, safe, and effective for users at scale.

As part of our diverse team-including data scientists, engineers, language engineers, linguists, and program managers-you will collaborate closely with science, engineering, and product teams. We are driven by customer obsession and a commitment to excellence.

Key job responsibilities

In this role, you will leverage data-centric AI principles to assess the impact of data on model performance and the broader machine learning pipeline. You will apply Generative AI techniques to evaluate how well our data represents human language and conduct experiments to measure downstream interactions.

Specific responsibilities include:

• Design and develop comprehensive evaluation and benchmarking datasets for Quick Suite AI-powered features
• Leverage LLMs for synthetic data corpora generation; data evaluation and quality assessment using LLM-as-a-judge settings
• Create ground truth datasets with high-quality question-answer pairs across diverse domains and use cases
• Lead human annotation initiatives and model evaluation audits to ensure data quality and relevance
• Develop and refine annotation guidelines and quality frameworks for evaluation tasks
• Conduct statistical analysis to measure model performance, identify failure patterns, and guide improvement strategies
• Collaborate with ML scientists and engineers to translate evaluation insights into actionable product improvements
• Build scalable data pipelines and tools to support continuous evaluation and benchmarking efforts
• Contribute to Responsible AI initiatives by developing safety and fairness evaluation datasets

About the team

Why AWS?

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Inclusive Team Culture

Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness.

Mentorship & Career Growth

We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.

Hybrid Work

We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.