Data Scientist II, Data Scientist, IES CFX & Prime

Description Are you passionate about applying machine learning and advanced statistical techniques to protect one of the world's largest online marketplaces? Do you want to be at the forefront of developing innovative solutions that improve Amazon's customers experience? Do you thrive in a collaborative environment where diverse perspectives drive breakthrough solutions?

If yes, we invite you to join the Amazon Customer Fulfillment & Prime analytics team. We're seeking an exceptional scientist who can revolutionize how we delight our customers. As a key member of our team, you'll develop and deploy machine learning systems that analyze millions of customer interactions daily, ensuring state of art experience for customers across 10 countries. Your work will directly impact the shopping experience for hundreds of millions of customers worldwide.

Key job responsibilities

• Ability to work independently across the end-to-end ML lifecycle: feature engineering, modeling, and MLOps to productionize models.
• Strong foundation in machine learning and statistical modeling.
• Experience deploying ML solutions into a production environment.
• Apply machine learning and statistical techniques to create scalable abuse-detection solutions that identify fraudulent customer behavior, rings of accounts, identity change, holistic customer risk, and marketplace manipulation schemes.
• Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions.
• Collaborate with cross-functional teams to develop comprehensive ML/statistical models that scale to millions of customers across 10+ countries.
• Willing to travel to Mexico (MX) and Brazil (BR).
• Comfortable working CET hours to maintain overlap with Mexico and Brazil business teams.

About The Team You'll be working closely with business partners, science and engineering teams to create end-to-end scalable machine learning solutions that address real-world problems. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation.

You will also be providing clear and compelling reports for your solutions and contributing to the ongoing innovation and knowledge-sharing that are central to the team's success.

Basic Qualifications

• 6+ years of data scientist experience
• 6+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 6+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
• 5+ years of working with or evaluating AI systems experience
• Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)

Preferred Qualifications

• Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
• Knowledge of machine learning concepts and their application to reasoning and problem-solving
• Experience in Python, Perl, or another scripting language
• 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

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.