Data Scientist, AWS Quick Data

Amazon • Santa Clara, CA

2d • Hybrid

About The Position

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, with
particular emphasis on Research and other generative AI capabilities. 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.

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.

Requirements

• 2+ years of data scientist experience
• 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• 1+ years of working with or evaluating AI systems experience
• 1+ years of creating or contributing to mathematical textbooks, research papers, or educational content experience
• Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
• Experience applying theoretical models in an applied environment

Nice To Haves

• Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
• Knowledge of machine learning concepts and their application to reasoning and problem-solving
• Experience in a ML or data scientist role with a large technology company
• Experience in defining and creating benchmarks for assessing GenAI model performance
• Experience working on multi-team, cross-disciplinary projects
• Experience applying quantitative analysis to solve business problems and making data-driven business decisions
• Experience effectively communicating complex concepts through written and verbal communication

Responsibilities

• 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

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level