Director of Training and Quality, Catalog Ops Improvement

Company Introduction

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we are collectively disrupting the multi-billion-dollar commerce industry from the ground up and establishing an unparalleled reputation for being leading and reliable force in South Korean commerce.

We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been at since our inception. We are all entrepreneurial surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.

Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.

Role Overview

As Director of Catalog Operations Improvement for our Catalog Operations team, you will play a critical role in ensuring the accuracy, efficiency, and continuous improvement of our labelling and audit operations. You will be responsible for developing and implementing training programs, defining quality metrics, and driving best practices to enhance data label accuracy and operational workflows. This role requires a strategic thinker with deep expertise in e-commerce Machine Learning Operations, and quality assurance methodologies.  You will collaborate closely with data scientists, ML engineers, and operation teams to optimize training protocols, ensure high-quality data inputs, and develop methodologies to measure and improve operator accuracy.

What You Will Do

Quality Assurance & Process Optimization

• Define and implement quality control processes to ensure the accuracy and reliability of labeled data and operational workflows.
• Establish and monitor key performance indicators (KPIs) related to data labelling accuracy, annotation consistency, and operational efficiency.
• Develop and enforce Standard Operation Procedures (SOPs) to ensure consistency, compliance, and best practices across ML operations.
• Lead root cause analysis and corrective action initiatives to address quality issues and optimize workflow performance.
• Work closely with product and engineering teams to enhance automation and improve data validation processes.

2. Training & Development

• Design, implement, and continuously improve training programs for ML annotators, reviewers, and operational teams.
• Develop competency frameworks and certification processes to ensure a high level of accuracy and consistency in ML training.
• Collaborate with ML engineers and data scientists to align training content with evolving ML models and workflows.
• Introduce new tools, techniques, and best practices for improving annotation efficiency and decision-making.

3. Tooling & Technology Enablement

• Assess, implement, and optimize tooling solutions for data annotation, quality assurance, and workflow automation.
• Work with engineering teams to refine and enhance annotation platforms
• Oversee integration of quality monitoring tools, dashboards, and analytics to track performance and drive insights.
• Evaluate and implement AI-driven solutions to improve annotation accuracy and reduce manual intervention.
• Ensure the seamless adoption of new tools through structured training and documentation.

4. Leadership & Stakeholder Collaboration

• Build and lead a team of quality specialists, trainers, and process analysts to support ML operations at scale.
• Partner with cross-functional teams – including ML engineers, data scientists and operation teams - to align training and quality initiatives with business goals.
• Stay up to date with industry trends, emerging ML technologies, and e-commerce best practices to drive continuous improvement.
• Advocate for a culture of operational excellence, data integrity, and continuous learning.

Basic Qualifications

• Strong understanding of machine learning concepts, data annotation processes, and model evaluation techniques.
• Experience in e-commerce operations, ML ops, or technical operation processes.
• Proven ability to design and implement quality assurance processes that enhance workforce capabilities.
• Analytical mindset with experience using data driven approaches to improve quality and efficiency.
• Proven ability to design and implement training programs that enhance workforce capabilities.
• Excellent leadership, communication, and stakeholder management skills.
• Proficiency in quality management methodologies (Six Sigma, Lean, etc)
• Experience with tooling selection and implementation for annotation, automation and performance monitoring.
• Strong expertise in developing, managing and enforcing Standard Operating Procedures for large-scale operations.

Preferred Qualifications

• 7+ years of experience in training, quality assurance, or operational leadership within an ML, AI or data driven environment.
• Experience working with ML annotation platforms (Scale AI, Labelbox, Amazon SageMaker Ground Truth)
• Familiarity with automation tools, workflow optimization, and AI-assisted quality assurance.
• Knowledge of compliance and ethical considerations in data labelling and ML training.
• Proficiency in scripting or dashboarding tools (SQL, Python, Tableau, Looker) to support quality tracking and analytics