Staff Data Scientist, Catalog

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.

About the Role

We are seeking a Senior Staff Scientist to join Coupang’s Catalog Data Analytics team. This is a newly created leadership role aimed at elevating the team’s scientific capabilities beyond current reporting and data engineering functions.

This role is ideal for a hands-on scientist who brings deep expertise in modeling, experimentation, and inference, and who thrives in cross-functional collaboration with engineering, product, and business teams. You will introduce advanced scientific methodologies to solve complex data problems, drive automation at scale, and unlock actionable insights that shape our catalog systems and operations.

Key Responsibilities

1. Science and Modeling Leadership

• Lead the transition of the Catalog Data Analytics team from descriptive reporting to predictive modeling, causal inference, and robust experimentation.
• Develop and deploy ML models for catalog health scoring, anomaly detection, and data quality prediction across millions of SKUs.
• Apply advanced techniques (e.g., transformers, foundation models, Bayesian inference) to improve structured/semi-structured product data accuracy and completeness.

1. Strategic Impact Projects

• Tracking Catalog Health: Design scientific frameworks and metrics to monitor catalog quality. Build predictive systems that proactively detect emerging data issues or content decay.
• Automating SOPs: Use data science, ML, and LLMs to automate repetitive catalog management processes and reduce manual errors in data curation and enrichment.

1.  Cross-Functional Collaboration

• Partner with the Backend Engineering team to build scalable, production-grade pipelines and integrate modeling solutions into core catalog services.
• Collaborate with Product Management and Business Leadership to define problem statements, prioritize initiatives, and ensure measurable business impact.
• Work closely with Data Analysts to elevate the team’s analytical rigor, guide experimental design, and support training in advanced scientific approaches.

1.  Mentorship and Culture Building

• Mentor data scientists and analysts to deepen the team’s scientific bench strength.
• Advocate for a culture of rigorous testing, peer review, and continuous learning.

Basic Qualifications

• MS or PhD in Computer Science, Statistics, Applied Mathematics, or a related quantitative field.
• 8+ years of experience in data science or applied research roles, preferably in high-scale technology or e-commerce environments.
• Demonstrated expertise in building predictive models, causal inference, and A/B testing at scale.
• Strong proficiency in Python and SQL; experience with large-scale data tools (e.g., Spark, Airflow) and ML frameworks (e.g., TensorFlow, PyTorch).
• Proven experience working closely with backend engineering and product teams to deploy solutions in production environments.

Preferred Qualifications

• Experience with structured product data, taxonomy systems, or catalog health in e-commerce or logistics.
• Background in applying LLMs or NLP to structured/semi-structured data environments.
• Strong ability to translate business problems into scientific questions and communicate findings to senior leadership.
• Prior experience in upskilling analytical teams or building scientific rigor in existing data organizations.