Principal Data Analyst (Retail Inventory)

Company Introduction

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did I ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion-dollar commerce industry.

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 since our inception. Our mission to build the future of commerce is real, and we push the boundaries of what’s possible. Join Coupang now to create an epic impact in this always-on, high-tech, and hyper-connected world.

About the Role

The Retail Inventory organization is the nerve center for Coupang's entire product selection. Our team manages the end-to-end lifecycle of tens of millions of items, balancing a multi-billion-dollar budget and complex operational constraints. We optimize everything from Inbound planning and vendor rebates, to protecting Inventory Health , to managing the economic strategies for Aged Inventory Clearance .

Are you excited by the challenge of building intelligent models that decide how to price distressed inventory, optimize stock levels across millions of items, or maximize complex vendor rebate structures?

This is not a typical analytics role. We are looking for a Principal Data Analyst to act as a strategic innovation partner for the entire Retail Inventory organization. Based in Seoul , you will be a remote member of our core strategy team in Seoul. Your mission is to leverage your deep expertise in analytics, data science, and e-commerce to design, build, and deploy solutions that solve our most complex business problems.

You will move beyond dashboards and reporting to create scalable, automated models and algorithms that directly impact profitability, working capital, and customer experience. We are looking for a builder with a strong commercial mindset, who has experience in the "Category Analytics" space and is passionate about applying advanced analytical methods to drive real-world business results.

What You Will Do

· Design, build, and deploy price elasticity models to optimize the sell-through and recovery value of identified cohort of SKUs.

· Collaborate with Marketing and CX teams to drive better views and conversion for identified set of SKUs, collaborate to create specific campaigns

· Develop and implement advanced optimization logic for Days of Cover (DOC) targets, balancing the trade-offs between out-of-stock risk, working capital, and operational capacity.

· Partner with our Rebates lead to model and simulate complex, multi-tier vendor contracts, building algorithms to identify the optimal purchase quantities that maximize rebate earnings while controlling overstock risk.

· Create and test automated discount algorithms for near-expiry or slow-moving products to maximize sales velocity and minimize financial loss.

· Act as a key thought leader, proactively identifying and modeling new opportunities for data science and algorithmic solutions to drive efficiency across the entire inventory lifecycle.

· Collaborate closely with senior leadership and functional experts in Inbound, Inventory Health, and Clearance to define problem statements, build solutions, and present clear, actionable recommendations.

· Translate complex models and quantitative insights into clear, concise business-facing narratives for stakeholders in finance, operations, and retail.

Basic Qualifications

· 8-10 years of experience in a "Category Analytics," "Decision Science," "Business Analytics," or "Data Science" role, preferably within an e-commerce, retail, or tech startup environment.

· Bachelor’s degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, Economics, Mathematics) or equivalent practical experience.

· Expert proficiency in SQL and Python for data manipulation, modeling, and analysis.

· Proven experience applying statistical, machine learning, or optimization models to solve real-world business problems (e.g., pricing, forecasting, inventory optimization, or commercial analytics).

· Strong business acumen and a commercial mindset; you are passionate about understanding how a business runs and using data to drive tangible results.

· Excellent communication skills, with the ability to explain complex models and analyses to non-technical audiences.

· A proactive, self-starting mentality, with the ability to manage projects from conception to completion in a fast-paced, remote-first (with the core team) environment.

Preferred Qualifications

· Engineering, Master’s or PhD in a quantitative field.

· Experience with data pipeline tools (e.g., Airflow) and large-scale data environments (e.g., Redshift, Hive, Spark).

· Experience with data visualization tools (Tableau, Power BI) for communicating insights.

· Deep understanding of supply chain, inventory