Catalog Specialist - Machine Learning Data Annotation

Catalog Specialist, WWRR Science Annotation Team

In the Worldwide Returns, ReCommerce & Sustainability (WW RR&S) group at Amazon, we are dedicated to ‘making zero happen’ – zero cost of returns, zero waste, and zero defects – to benefit our customers, company, and environment. We are an agile and inclusive organization that constantly innovates to create long-term value by investing in our people and our planet, not simply focusing on the bottom line.

WW R&R includes business, product, operations, data, and software engineering teams, who together manage the lifecycle of returned and damaged products. In WW R&R, you will partner across these teams to help customers discover great deals on quality used, rentals, and open box items; get the most value out of Amazon’s products; improve the customer returns experience; and reduce defects, waste, and cost in reverse logistics processes. You will be a leader, a builder, and an owner, collaborating cross-functionally with technical, operations, and business teams to design scalable and automated solutions to customer problems.

Amazon is Earth’s most customer-centric company and in WW R&R, the Earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns, ReCommerce & Sustainability team!
We are hiring an experienced Catalog Specialist to help us grow our business in innovative ways. In this role, you will work closely with our product, technology and science teams to support new Machine Learning (ML) models and data science classification algorithm development – all helping to delight our customers through new experiences throughout their Amazon shopping journey.
Need candidates in language proficiency in: Spanish

Key job responsibilities
• Work closely with our product, technology, and science teams to support Machine Learning (ML) models
• Perform data annotation required to train and evaluate ML models effectively
• Support data scientists in the development of classification algorithms
• Collaborate with cross-functional teams to ensure data annotation tasks align with project objectives and timelines
• Maintain high-quality standards for annotated data to optimize model performance
• Continuously evaluate and improve annotation processes to enhance efficiency and accuracy
• Strong analytical skills and the ability to deep-dive on complex problems
• Ability to manage multiple simultaneous projects requiring frequent communication, organization/time management and problem-solving skills

Basic Qualifications

- • Work closely with our product, technology, and science teams to support Machine Learning (ML) models
- • Perform data annotation required to train and evaluate ML models effectively
- • Support data scientists in the development of classification algorithms
- • Collaborate with cross-functional teams to ensure data annotation tasks align with project objectives and timelines
- • Maintain high-quality standards for annotated data to optimize model performance
- • Continuously evaluate and improve annotation processes to enhance efficiency and accuracy
- • Strong analytical skills and the ability to deep-dive on complex problems
- • Ability to manage multiple simultaneous projects requiring frequent communication, organization/time management and problem-solving skills

Preferred Qualifications

- Preferred qualifications
- Bachelor's degree or above in computer science, computer engineering, or related field, or experience in communicating technically, at a level appropriate for the audience
- Proven experience in data annotation and labeling for ML model training and evaluation
- Demonstrated ability to adapt to evolving technologies and methodologies in the ML domain

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.

Experience Level

Senior Level