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Sr~Staff Data Analyst (Coupang Eats)

External
coupang logoCoupang ยท Seoul, South Korea
Full-timeOn-site2w ago
Data AnalysisData ModelingETLForecastingMachine LearningPython
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Vision insurance

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Eats Analytics ํŒ€ ์†Œ๊ฐœ ๐Ÿš€ Eats Analytics ํŒ€์˜ ๋น„์ „์€ ๋ฐ์ดํ„ฐ, ๋ถ„์„, ๊ทธ๋ฆฌ๊ณ  ์ ์šฉํ˜• AI๋ฅผ ํ†ตํ•ด ๋ณต์žกํ•œ ๋น„์ฆˆ๋‹ˆ์Šค ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•จ์œผ๋กœ์จ , Coupang Eats์˜ ๋ชจ๋“  ์˜์‚ฌ๊ฒฐ์ •์ด ๋ฐ์ดํ„ฐ์— ๊ธฐ๋ฐ˜ํ•˜๊ณ  ์‹ค์งˆ์ ์ธ ์ž„ํŒฉํŠธ๋ฅผ ์ฐฝ์ถœํ•˜๋„๋ก ๋งŒ๋“œ๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ ํŒ€์€ ๋‹จ์ˆœํ•œ ๋ฆฌํฌํŒ…์ด๋‚˜ ์šด์˜ ์ง€์›์„ ๋„˜์–ด์„ญ๋‹ˆ๋‹ค. ๋น„์ฆˆ๋‹ˆ์Šค, ํ”„๋กœ๋•ํŠธ, ์šด์˜ ๋ฆฌ๋”๋“ค๊ณผ ๊ธด๋ฐ€ํ•˜๊ฒŒ ํ˜‘์—…ํ•˜์—ฌ ๋ฌธ์ œ๋ฅผ ์ •์˜ํ•˜๊ณ , ๋ถ„์„ ๋ฐ ๋ฐ์ดํ„ฐ ๋ชจ๋ธ์„ ๊ตฌ์ถ•ํ•˜๋ฉฐ, ์ธ์‚ฌ์ดํŠธ๋ฅผ ๋Œ€๊ทœ๋ชจ๋กœ ์šด์˜์— ์ ์šฉํ•ฉ๋‹ˆ๋‹ค. Coupang Eats์˜ ๋ฐ์ดํ„ฐ ๋ถ„์„์€ ๋น„์ฆˆ๋‹ˆ์Šค ์ „๋žต, ์‹คํ—˜(Experimentation), ์˜ˆ์ธก(Forecasting), ๊ทธ๋ฆฌ๊ณ  AI ๊ธฐ๋ฐ˜ ์˜์‚ฌ๊ฒฐ์ • ์ „๋ฐ˜์— ๊ฑธ์ณ ์žˆ์œผ๋ฉฐ, ํŒ€์›๋“ค์€ Data Science, Machine Learning, ํ˜น์€ ๊ณ ๊ธ‰ ๋น„์ฆˆ๋‹ˆ์Šค ์• ๋„๋ฆฌํ‹ฑ์Šค ์—ญํ• ๋กœ ์„ฑ์žฅํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ๋ฅผ ์–ป๊ฒŒ ๋ฉ๋‹ˆ๋‹ค. Data Analyst๋กœ์„œ ๋‹น์‹ ์€ ๋ฌธ์ œ ์ •์˜๋ถ€ํ„ฐ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ธ์‚ฌ์ดํŠธ ๋„์ถœ, ์‹ค์ œ ์šด์˜ ์‹คํ–‰๊นŒ์ง€ ์—”๋“œํˆฌ์—”๋“œ๋กœ ๊ด€์—ฌ ํ•˜๊ฒŒ ๋˜๋ฉฐ, ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ, ์‹คํ—˜ ํ”Œ๋žซํผ, AI ๊ธฐ๋ฐ˜ ๋น„์ฆˆ๋‹ˆ์Šค ํ™œ์šฉ ์‚ฌ๋ก€๋ฅผ ํญ๋„“๊ฒŒ ๊ฒฝํ—˜ํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค. ์ฃผ์š” ์—…๋ฌด ๐Ÿš€ ๋ฐฐ๋‹ฌ ์šด์˜, ๋จธ์ฒœํŠธ ์ƒํƒœ๊ณ„, ๊ณ ๊ฐ ์„ฑ์žฅ, ๋งˆ์ผ“ํ”Œ๋ ˆ์ด์Šค ๊ตฌ์กฐ ์ „๋ฐ˜์— ๊ฑธ์ณ ๋ฐ์ดํ„ฐ-๋ถ„์„-์ ์šฉํ˜• AI๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ๋น„์ฆˆ๋‹ˆ์Šค ๋ฌธ์ œ ํ•ด๊ฒฐ ๋ชจํ˜ธํ•œ ๋น„์ฆˆ๋‹ˆ์Šค ์งˆ๋ฌธ์„ ๋ถ„์„ ํ”„๋ ˆ์ž„์›Œํฌ์™€ ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋กœ ๊ตฌ์กฐํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๋น„์ฆˆ๋‹ˆ์Šค ๋ฐ ํ”„๋กœ๋•ํŠธ ๋ฆฌ๋”์™€ ํ˜‘์—… ๋Œ€๊ทœ๋ชจ A/B ํ…Œ์ŠคํŠธ์™€ ์‹คํ—˜์„ ์„ค๊ณ„, ์‹คํ–‰, ๋ถ„์„ํ•˜๋ฉฐ ๋‹จ๊ธฐ ์„ฑ๊ณผ์™€ ์žฅ๊ธฐ ๋น„์ฆˆ๋‹ˆ์Šค ์˜ํ–ฅ ํ‰๊ฐ€ ์šด์˜ ๋ฐ ์ „๋žต์  ์˜์‚ฌ๊ฒฐ์ •์„ ์ง€์›ํ•˜๋Š” ๋ถ„์„ ๋ฐ์ดํ„ฐ์…‹, ํŒŒ์ดํ”„๋ผ์ธ, ๋ชจ๋ธ(์˜ˆ: ์˜ˆ์ธก, ์ตœ์ ํ™”, ์„ธ๊ทธ๋ฉ˜ํ…Œ์ด์…˜) ๊ฐœ๋ฐœ ๋ฐ ์œ ์ง€ ํ†ต๊ณ„ ๋ถ„์„, ๋ชจ๋ธ๋ง, ์‹คํ—˜์„ ํ†ตํ•ด ๋ฐฐ๋‹ฌ ์‹œ๊ฐ„, ๋น„์šฉ ํšจ์œจ, ์ˆ˜์š”-๊ณต๊ธ‰ ๊ท ํ˜•, ๊ณ ๊ฐ ์œ ์ง€์œจ ๋“ฑ ํ•ต์‹ฌ ๋น„์ฆˆ๋‹ˆ์Šค ์ง€ํ‘œ ์ตœ์ ํ™” ๋ชจ๋ธ๊ณผ ์ธ์‚ฌ์ดํŠธ๋ฅผ ์ผ์ƒ์ ์ธ ์˜์‚ฌ๊ฒฐ์ • ํ”„๋กœ์„ธ์Šค์— ์ ์šฉํ•˜์—ฌ ์šด์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๊ณ , ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก ์„ ์ง€์†์ ์œผ๋กœ ๊ฐœ์„  ๋‹จ์ˆœํ•œ ๊ณผ๊ฑฐ ๋ฆฌํฌํŒ…์ด ์•„๋‹Œ ๋ฏธ๋ž˜์ง€ํ–ฅ์  ์ธ์‚ฌ์ดํŠธ ์ œ๊ณต ๊ณผ ํ•ต์‹ฌ ์ง€ํ‘œ ์˜ค๋„ˆ์‹ญ ํ™•๋ณด ๊ณ ๊ฐ, ๋จธ์ฒœํŠธ, ์šด์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์‹ฌ์ธต ๋ถ„์„ํ•˜์—ฌ ํ™•์žฅ ๊ฐ€๋Šฅํ•œ ์„ฑ์žฅ ๊ธฐํšŒ ๋ฐœ๊ตด ๊ธฐ์ˆ ์  ๋ฐฐ๊ฒฝ์ด ์—†๋Š” ์ดํ•ด๊ด€๊ณ„์ž์—๊ฒŒ๋„ ๋ช…ํ™•ํ•˜๊ฒŒ ์ธ์‚ฌ์ดํŠธ์™€ ์ œ์•ˆ์„ ์ „๋‹ฌํ•˜๋ฉฐ, ๋‹ค์–‘ํ•œ ์กฐ์ง ๋ ˆ๋ฒจ์˜ ์˜์‚ฌ๊ฒฐ์ •์— ์˜ํ–ฅ๋ ฅ ํ–‰์‚ฌ ํ•ต์‹ฌ ์„ฑ๊ณผ ์ง€ํ‘œ (KPI) ๋ถ„์„, ์‹คํ—˜, ๋ชจ๋ธ์„ ํ†ตํ•ด ์ฐฝ์ถœ๋œ ์‹ค์งˆ์ ์ธ ๋น„์ฆˆ๋‹ˆ์Šค ์ž„ํŒฉํŠธ ๊ณ ๊ฐ, ๋จธ์ฒœํŠธ, ์šด์˜ ๊ด€๋ จ KPI ๊ฐœ์„  (์œ ์ง€์œจ, ํšจ์œจ์„ฑ, ์œ ๋‹› ์ด์ฝ”๋…ธ๋ฏน์Šค ๋“ฑ) ์ผ์ƒ ์šด์˜์—์„œ ๋ถ„์„ ๋ชจ๋ธ๊ณผ ์ธ์‚ฌ์ดํŠธ์˜ ํ™œ์šฉ๋„ ๋ฐ ํšจ๊ณผ์„ฑ ์ž๊ฒฉ ์š”๊ฑด ๐Ÿš€ ๋น„์ฆˆ๋‹ˆ์Šค ๋ฌธ์ œ ํ•ด๊ฒฐ์„ ์œ„ํ•ด ๋ฐ์ดํ„ฐ ๋ถ„์„, ์• ๋„๋ฆฌํ‹ฑ์Šค, ๋˜๋Š” ๋ฐ์ดํ„ฐ ์‚ฌ์ด์–ธ์Šค๋ฅผ ์ ์šฉํ•œ 5๋…„ ์ด์ƒ์˜ ๊ฒฝ๋ ฅ SQL์— ๋Œ€ํ•œ ๊ฐ•ํ•œ ์ˆ™๋ จ๋„์™€ ETL, ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋ง, ๋Œ€๊ทœ๋ชจ ๋ฐ์ดํ„ฐ์…‹ ๊ฒฝํ—˜ ํŠน์ • ๋น„์ฆˆ๋‹ˆ์Šค ๋„๋ฉ”์ธ, ํ”„๋กœ๋•ํŠธ ์˜์—ญ, ๋˜๋Š” ์šด์˜ ๊ธฐ๋Šฅ์— ๋Œ€ํ•œ ๋ถ„์„ ์˜ค๋„ˆ์‹ญ ๊ฒฝํ—˜ ๋ฐ์ดํ„ฐ ์ธ์‚ฌ์ดํŠธ๋ฅผ ๋น„์ฆˆ๋‹ˆ์Šค ์„ฑ๊ณผ๋กœ ์—ฐ๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ๋›ฐ์–ด๋‚œ ๋ถ„์„์  ์‚ฌ๊ณ ๋ ฅ ๋น ๋ฅด๊ฒŒ ๋ณ€ํ™”ํ•˜๊ณ  ๋ถˆํ™•์‹คํ•œ ํ™˜๊ฒฝ์—์„œ ๋‹ค์–‘ํ•œ ์กฐ์ง๊ณผ ํ˜‘์—…ํ•œ ๊ฒฝํ—˜ ์—…๋ฌด ์–ธ์–ด๋กœ์„œ ์˜์–ด ์‚ฌ์šฉ์— ๋Œ€ํ•œ ํŽธ์•ˆํ•จ ์šฐ๋Œ€ ์‚ฌํ•ญ ๐Ÿš€ ์ •๋Ÿ‰์  ์ „๊ณต(STEM, ๊ธˆ์œต, ๊ฒฝ์ œํ•™, ํ†ต๊ณ„ ๋“ฑ) ํ•™์‚ฌ ํ•™์œ„ ๋ฐ์ดํ„ฐ ๋ถ„์„, ๋ชจ๋ธ๋ง, ์‹คํ—˜์„ ์œ„ํ•œ Python ํ™œ์šฉ ๊ฒฝํ—˜ ์˜ˆ์ธก, ์ตœ์ ํ™”, ๋˜๋Š” ์ ์šฉํ˜• ๋จธ์‹ ๋Ÿฌ๋‹ ๋ชจ๋ธ ๊ฒฝํ—˜ ์ด์ปค๋จธ์Šค, ํ‘ธ๋“œ ๋”œ๋ฆฌ๋ฒ„๋ฆฌ, ๋ฌผ๋ฅ˜, ๋งˆ์ผ“ํ”Œ๋ ˆ์ด์Šค ํ”Œ๋žซํผ ๊ฒฝํ—˜ Data Science, ๊ณ ๊ธ‰ ์• ๋„๋ฆฌํ‹ฑ์Šค, AI ๊ธฐ๋ฐ˜ ๋น„์ฆˆ๋‹ˆ์Šค ์—ญํ• ๋กœ์˜ ์„ฑ์žฅ์— ๋Œ€ํ•œ ๊ด€์‹ฌ ์„์‚ฌ(MS) ๋˜๋Š” MBA ํ•™์œ„ ๋ณด์œ ์ž ์šฐ๋Œ€ Equal Opportunities for All Coupang is an equal opportunity employer. Our unprecedented success could not be possible without the valuable inputs of our globally diverse team. About Eats Analytics Team ๐Ÿš€ The vision of the Eats Analytics Team is to solve complex business problems through data, analytics, and applied AI , ensuring that every decision at Coupang Eats is both data-informed and impact-driven. Our team goes beyond traditional reporting or operational support. We work closely with business, product, and operations leaders to define problems, build analytical and data models, and operationalize insights at scale . Analytics at Coupang Eats spans business strategy, experimentation, forecasting, and AI-driven decision making , providing team members with opportunities to grow toward Data Science, Machine Learning, or advanced Business Analytics roles. As a Data Analyst, you will work end-to-end-from problem definition to model-driven insights to real-world operational execution-while gaining exposure to large-scale data, experimentation platforms, and AI-enabled business use cases. Responsibilities ๐Ÿš€ Solve diverse business problems using data, analytics, and applied AI across delivery operations, merchant ecosystem, customer growth, and marketplace dynamics Partner with business and product leaders to translate ambiguous business questions into analytical frameworks and data models Design, execute, and analyze large-scale A/B tests and experiments , evaluating short-term impact and long-term business implications Develop and maintain analytical datasets, pipelines, and models (e.g., forecasting, optimization, segmentation) that support both operational and strategic decision-making Apply statistical analysis, modeling, and experimentation to optimize core business metrics such as delivery time, cost efficiency, demand-supply balance, and customer retention Enable operational excellence by operationalizing models and insights into daily decision workflows , while continuously improving methodologies Own key metrics and provide forward-looking insights rather than purely retrospective reporting Conduct deep-dive analyses on customer, merchant, and operational data to identify scalable growth opportunities Communicate findings and recommendations clearly to non-technical stakeholders, influencing decisions at multiple organizational levels Key Performance Indicators Business impact driven by analytics, experiments, and models Improvement in customer, merchant, and operational KPIs (retention, efficiency, unit economics) Adoption and effectiveness of analytical models and insights in daily operations Basic Qualifications ๐Ÿš€ 5+ years of experience applying analytics, data analysis, or data science to solve business problems Strong SQL skills and experience with ETL, data modeling, and large-scale datasets Experience owning analytics for a business domain, product area, or operational function Strong analytical thinking with the ability to connect data insights to business outcomes Experience working in fast-paced, ambiguous environments with cross-functional stakeholders Comfortable using English as a working language Preferred Experience ๐Ÿš€ Bachelor's degree in a quantitative field (STEM, Finance,


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