Senior/Staff Data Scientist Customer Experience Product Analytics

Seoul, South Korea

Coupang

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About the Team ๐Ÿš€

Customer Experience (CX) Product Analytics ํŒ€์€ ๊ณ ๊ฐ๋“ค์˜ ์ด์ปค๋จธ์Šค ์—ฌ์ •์—์„œ ๋ชจ๋ฐ”์ผ/์›น ํ”„๋กœ๋•ํŠธ์™€ ์ƒํ˜ธ ์ž‘์šฉํ•˜๋Š” ๋ฐฉ์‹์„ ๊ฐœ์„ ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ๋˜ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ฉํ•˜์—ฌ ์‹คํ–‰ ๊ฐ€๋Šฅํ•œ ์ธ์‚ฌ์ดํŠธ๋ฅผ ์ƒ์„ฑํ•˜๊ณ , ๋ณต์žกํ•œ ๋น„์ฆˆ๋‹ˆ์Šค ๋ฌธ์ œ์— ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜๋ฉฐ ์ฟ ํŒก์˜ ๊ณ ๊ฐ์— ๋Œ€ํ•œ ๊นŠ์€ ์ดํ•ด๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค.

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Responsibilities ๐Ÿš€

ยท Product ์ „๋žต ๊ฐœ๋ฐœ/์ง€์›์„ ์œ„ํ•œ ์ •๋Ÿ‰์  ๋ถ„์„, ์‹คํ—˜ ๋ฐ ๋ฐ์ดํ„ฐ ์ œ์‹œ๋ฅผ ํ†ตํ•ด ๊ธฐ์ˆ ์  ์ „๋ฌธ์„ฑ์„ ์ ์šฉํ•˜๋Š” ๋™์‹œ์—, Product ๊ฐœ์„ ์„ ์œ„ํ•œ ๊ธฐ๋Œ€์™€ ์ˆ˜๋‹จ์„ ์ •์˜, ์ดํ•ด, ํ…Œ์ŠคํŠธํ•˜๊ณ  ํ†ต์ฐฐ๋ ฅ์— ๊ธฐ๋ฐ˜ํ•œ ์ถ”๊ฐ€์ ์ธ ์ œ์•ˆ์„ ํ†ตํ•ด ๋กœ๋“œ๋งต์„ ์ถ”์ง„ํ•ฉ๋‹ˆ๋‹ค.

ยท ๊ฐ€์„ค๊ฒ€์ฆ - ํ…Œ์ŠคํŠธ ๊ฐ€์„ค์„ ์„ธ์šฐ๊ณ , ๊ธฐํšŒ๋ฅผ ๊ฒ€์ฆํ•˜๊ณ  ๊ณ ๊ฐ ๊ฒฝํ—˜๊ณผ ๋น„์ฆˆ๋‹ˆ์Šค KPI์— ๊ธ์ •์ ์ธ ๊ฐœ์„ ์„ ๊ฐ€์ ธ๋‹ค ์ค„ ์ˆ˜ ์žˆ๋Š” ์กฐ์น˜๋“ค์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค.

ยท ํ…Œ์ŠคํŠธ ๋ถ„์„ - ํ†ต๊ณ„ํ•™์ ์œผ๋กœ ์ •ํ™•ํ•˜๊ณ  ์ ํ•ฉํ•œ A/B analysis ์„ ๋ฆฌ๋”ฉํ•˜๊ณ , ํ…Œ์ŠคํŠธ ๊ฒฐ๊ณผ๋ฅผ ์ด๋Œ์–ด๋‚ด๋Š” ์š”์ธ์— ๋Œ€ํ•œ โ€œWHYโ€์— ๋‹ตํ•˜๊ธฐ ์œ„ํ•ด ์ฝ”ํ˜ธํŠธ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•ฉ๋‹ˆ๋‹ค. ๋ถ„์„์ ์ด๊ณ  ํ†ต์ฐฐ๋ ฅ ์žˆ๋Š” Report ๊ด€์ ์—์„œ ์ข€ ๋” ๋ณต์žกํ•œ Test๋ฅผ ๊ณ ์•ˆํ•ฉ๋‹ˆ๋‹ค.

ยท ์ฃผ์š” Data Artifacts ์™€ Lineage (ex: ETL, ๋ฐ์ดํ„ฐ ๋ชจ๋ธ, ์ฟผ๋ฆฌ) ๋ฅผ ์œ ์ง€ํ•˜๋ฉฐ, ์ง„ํ–‰์ค‘์ธ ํ…Œ์ŠคํŠธ ์„ฑ๋Šฅ/๊ฒฐ๊ณผ ๋ชจ๋‹ˆํ„ฐ๋ง์„ ์œ„ํ•ด ์ž๋™ํ™”๋œ ๋ฐ์ดํ„ฐ ์›Œํฌํ”Œ๋กœ์šฐ์™€ ๋Œ€์‹œ๋ณด๋“œ๋ฅผ ๊ฐœ๋ฐœํ•ฉ๋‹ˆ๋‹ค.

ยท ๊ด€๋ จ ์‹œ๊ฐํ™” ํˆด (Tableau/PowerBI/Superset ๋“ฑ) ์„ ํ™œ์šฉํ•˜์—ฌ Metrics์„ ํŠธ๋ž˜ํ‚นํ•˜๊ณ , ์ด์ƒ ๋ฐ์ดํ„ฐ๋ฅผ ์กฐ์‚ฌํ•˜๋ฉฐ, ์ ์ ˆํ•œ ์ฐจ์›์—์„œ ์ง€ํ‘œ๋ฅผ ์ถ”๊ฐ€์ ์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ ์‹ฌ์ธต์ ์ธ dynamics์„ ๋ฐํ˜€๋ƒ…๋‹ˆ๋‹ค.

ยท ๋น„์ฆˆ๋‹ˆ์Šค์˜ ๊ธฐ์ˆ ์  ๋ฐ ์šด์˜์ ์ธ ๋ฉด์—์„œ์˜ ์„ธ๋ถ€ ์กฐ๊ฑด์‚ฌํ•ญ(ex: key dependencies, business drivers/KPIs, develop actionable business insights ๋“ฑ)๋“ค์„ ๋ฉด๋ฐ€ํžˆ ํƒ์ƒ‰ํ•ด์„œ ๊ฑด์„ค์ ์ธ ๊ธฐ์ˆ ์  ๋…ผ์˜ ๋ฐ ๊ฒ€์ฆ์— ๊ธฐ์—ฌํ•ฉ๋‹ˆ๋‹ค.

ยท ๊ธฐ์ˆ ์ /์‹œ์Šคํ…œ์ ์œผ๋กœ ๋” ํšจ์œจ์ ์ธ ์†”๋ฃจ์…˜์„ ์•Œ์•„๋ณด๊ณ  ํ…Œ์ŠคํŠธํ•ฉ๋‹ˆ๋‹ค. ๋ฐ์ดํ„ฐ๋ฅผ ์–ด๋–ป๊ฒŒ ์†Œํ™”, ์ฒ˜๋ฆฌ, ๋ถ„์„ํ•˜๋Š”์ง€ ์ดํ•ดํ•ฉ๋‹ˆ๋‹ค.

ยท ๋ฐ์ดํ„ฐ์…‹ ํ€„๋ฆฌํ‹ฐ๋ฅผ ๊ฐœ์„ ํ•˜๊ณ  ๋งค๋‰ด์–ผ ํ”„๋กœ์„ธ์Šค๋ฅผ ์ž๋™ํ™”ํ•ฉ๋‹ˆ๋‹ค.

ยท ํ”„๋กœ๋•ํŠธ ํŒ€์˜ ๋น„์ฆˆ๋‹ˆ์Šค ๊ฒฐ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ธ์‚ฌ์ดํŠธ์™€ ์†”๋ฃจ์…˜์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค.

ยท ์ดํ•ด๊ด€๊ณ„์ž๋“ค๊ณผ ์ œ์•ˆ๊ณผ ๋ฐœ๊ฒฌ์ ์— ๋Œ€ํ•ด ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ํ•˜๊ณ  ์ถ”ํ›„ ์‚ฌ์šฉ๊ณผ ๋ฐฐํฌ๋ฅผ ์œ„ํ•ด ์œ„ํ‚ค๋กœ ๋ฌธ์„œํ™”ํ•ฉ๋‹ˆ๋‹ค.

ยท ์ง๋ฌด์˜ Seniority์— ๋”ฐ๋ผ CX ํ”„๋กœ๋•ํŠธ ๋‚ด์— ๋ณต์žก์„ฑ์ด ๋†’์€ ๋ถ„์„์  ์˜์—ญ์„ ๋‹ด๋‹นํ•˜๊ฒŒ ๋˜๊ณ , ์—ฌ๋Ÿฌ PM๋“ค๊ณผ ์ƒํ˜ธ์ ์œผ๋กœ ์—…๋ฌดํ•˜๋ฉฐ ํ”„๋Ÿฌ๋•ํŠธ ๋ถ„์„๋ฐ ๋น„์ง€๋‹ˆ์Šค์˜ ์šฐ์„ ์ˆœ์œ„ ๊ฒฐ์ •์— ์ค‘์š”ํ•œ ์˜ํ–ฅ๋ ฅ์„ ๋ฐœํœ˜ ํ• ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

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Basic Qualifications ๐Ÿš€

ยท Querying Language (ex. SQL) ์™€ ์Šคํฌ๋ฆฝํŠธ ์–ธ์–ด (ํŒŒ์ด์ฌ) ์‚ฌ์šฉ ๊ฒฝํ—˜.

ยท Spark์— ๋Œ€ํ•œ ์ดํ•ด๋„๋ฅผ ๋ณด์œ ํ•˜์‹  ๋ถ„, GPU (ex. RapidsAI CUDA) ์‚ฌ์šฉ ๊ฒฝํ—˜ (์šฐ๋Œ€ ์‚ฌํ•ญ)

ยท ์ •๋Ÿ‰๋ถ„์•ผ์˜ ํ•™์‚ฌ (STEM, Finance, Economics, Statistics)

ยท Data Analyst, Data Scientist ๋“ฑ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ธฐ์ˆ ์„ ํ•„์š”๋กœํ•˜๋Š” ์—…๋ฌด ์•ฝ 3๋…„ ์ด์ƒ์˜ ๊ฒฝํ—˜

ยท A/B ํ…Œ์ŠคํŠธ์™€ Bayesian statistics์„ ํฌํ•จํ•œ A/B ํ…Œ์ŠคํŠธ์˜ ํ†ต๊ณ„ ๊ฐœ๋…์— ๋Œ€ํ•œ ์ดํ•ด๋„์™€, A/B ํ…Œ์ŠคํŠธ ์„ค๊ณ„, ๊ฒฐ๊ณผ ํ•ด์„ ๊ฒฝํ—˜

ยท ๋ถ„์‚ฐ ์‹œ์Šคํ…œ, ๋ฐ์ดํ„ฐ ๋ชจ๋ธ๋ง, scientific methods ์— ๋Œ€ํ•œ ์ข…ํ•ฉ์ ์ธ ์ดํ•ด๊ฐ€ ์žˆ๊ณ , ๊ธฐ์ˆ  ํ†ต๊ณ„/์ถ”๋ฆฌ ํ†ต๊ณ„์— ๋Œ€ํ•œ ๋†’์€ ์ „๋ฌธ์„ฑ์„ ๋ณด์œ .

ยท ๋‹ค์–‘ํ•œ stakeholders์„ ๋Œ€์ƒ์œผ๋กœ ๋‹ค์ˆ˜์˜ ํ”„๋ ˆ์  ํ…Œ์ด์…˜์„ ์ง„ํ–‰ํ•ด๋ณธ ๊ฒฝํ—˜ ๋ฐ ํšจ์œจ์ ์ธ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ์Šคํ‚ฌ์„ ๋ณด์œ .

ยท ์—…๋ฌด์˜ ์šฐ์„  ์ˆœ์œ„๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์ •ํ•˜๊ณ , ๋น ๋ฅด๊ณ  ์—ญ๋™์ ์ธ ํ™˜๊ฒฝ์—์„œ ํšจ๊ณผ์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์—ญ๋Ÿ‰ (๋ฉ€ํ‹ฐํ…Œ์Šคํ‚น ์—ญ๋Ÿ‰)

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Preferred Experience ๐Ÿš€

ยท ๊ณตํ•™ ๋ฐ ๋น„์ง€๋‹ˆ์Šค ํ•™์œ„ (์„/๋ฐ•์‚ฌ or MBA)

ยท ํ†ต๊ณ„ ๋ถ„์„์„ ์œ„ํ•œ R/SAS ํ™œ์šฉ ์—ญ๋Ÿ‰

ยท Hive, Presto, Airflow

ยท ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” (์˜ˆ: Tableau, Qlik, Looker, Power BI)

ยท ์ž์—ฐ์–ด (NLP) ์ฒ˜๋ฆฌ ๊ฒฝ๋ ฅ ์šฐ๋Œ€

ยท ์˜์–ด ๋Šฅํ†ต์ž ์šฐ๋Œ€

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์ „ํ˜• ์ ˆ์ฐจ ๋ฐ ์•ˆ๋‚ด ์‚ฌํ•ญ

์ „ํ˜•์ ˆ์ฐจ: ์„œ๋ฅ˜์ „ํ˜• - ๊ณผ์ œ ์ฝ”๋”ฉ ํ…Œ์ŠคํŠธ - ์ „ํ™”๋ฉด์ ‘ - ๋น„๋Œ€๋ฉด ํ™”์ƒ๋ฉด์ ‘ โ€“์ตœ์ข…ํ•ฉ๊ฒฉ

์ „ํ˜•์ ˆ์ฐจ๋Š” ์ง๋ฌด๋ณ„๋กœ ๋‹ค๋ฅด๊ฒŒ ์šด์˜๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ผ์ • ๋ฐ ์ƒํ™ฉ์— ๋”ฐ๋ผ ๋ณ€๋™๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.์ „ํ˜•์ผ์ • ๋ฐ ๊ฒฐ๊ณผ๋Š” ์ง€์›์„œ์— ๋“ฑ๋กํ•˜์‹  ์ด๋ฉ”์ผ๋กœ ๊ฐœ๋ณ„ ์•ˆ๋‚ด ๋“œ๋ฆฝ๋‹ˆ๋‹ค.

์ฐธ๊ณ ์‚ฌํ•ญ ๋ณธ ๊ณต๊ณ ๋Š” ๋ชจ์ง‘ ์™„๋ฃŒ ์‹œ ์กฐ๊ธฐ๋งˆ๊ฐ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.์ง€์›์„œ ๋‚ด์šฉ ์ค‘ ํ—ˆ์œ„์‚ฌ์‹ค์ด ์žˆ๋Š” ๊ฒฝ์šฐ์—๋Š” ํ•ฉ๊ฒฉ์ด ์ทจ์†Œ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.๋ณดํ›ˆ๋Œ€์ƒ์ž ๋ฐ ์žฅ์• ์ธ ์—ฌ๋ถ€๋Š” ์ฑ„์šฉ ๊ณผ์ •์—์„œ ์–ด๋– ํ•œ ๋ถˆ์ด์ต๋„ ๋ฏธ์น˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค.

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Sr./Staff Data Scientist_Customer Experience Product Analytics and Decision Science / Location: Seoul, Korea

Customer Experience Product Analytics and Decision Science (CX PA & DS) / Seoul, Korea

Coupang is one of the largest and fastest growing e-commerce platforms on the planet. We are on a mission to revolutionize everyday lives for our customers, employees and partners. We solve problems no one has solved before to create a world where people ask, โ€œHow did we ever live without Coupang?โ€ Coupang is a global company with offices in Beijing, Los Angeles, Seattle, Seoul, Shanghai, and Silicon Valley.

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About us :

Weโ€™re part of Customer Experience Product team, which aims to improve how our customers interact with your mobile/web products with their e-commerce journey.

Weโ€™re responsible for providing support in utilising decision science techniques in product development, along with discovery of customer behaviours within our products, in journey of creating better customer-facing products.

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Qualifications :

ยท Wealth of experience in using querying language(e.g. SQL) and scripting language (Python) with understanding of Spark, experiencing of utilising GPU(e.g. RapidsAI, CUDA) for is a plus.

ยท Understanding of A/B tests and its statistical concepts, including Bayesian statistics, with experience in designing and interpretation of A/B test results

ยท Having a comprehensive understanding of distributed systems, data modelling, and scientific methods. Highly proficient in descriptive statistics and inferential statistics

ยท Good presentation and communication skills in explaining data, as we often engage non-data savvy stakeholders

ยท Having inquisitive mindset- should be ready to dive into the unknown, discover, and share findings with others, while employing critical thinking and detail-oriented focus to solve ambiguous and unstructured problems

ยท Good command of English is a plus

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What you will do with us :

ยท Apply technical expertise with quantitative analysis, experimentation, and the presentation of data to aid the development of strategies for our products, while define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.

ยท Validate hypotheses โ€“ generate test hypotheses, validate opportunities and recommend actions that can have positive improvements in customer experience and business KPIs.

ยท Test Analysis โ€“ Drive proper A/B analysis with statistical rigor and perform cohort studies to answer โ€œwhy?โ€ on test result drivers. Ideate more complex tests from an analytical and insightful reporting perspective.

ยท Develop automated data workflows and dashboards to monitor ongoing test performance/results, with maintaining key data artifacts and lineage (e.g., ETL, data models, queries)

ยท Utilizes relevant visualization tools (Tableau/PowerBI/Superset/etc) to help track metrics and investigate data anomalies, and further segment metrics along suitable dimensions to reveal deeper dynamics

ยท Dive deeply into technical and operational details of the business (e.g., key dependencies, business drivers/KPIs, develop actionable business insights, etc.) and contribute to constructive technical discussions

ยท Explore and test more technically/computationally efficient solutions. Know how to ingest, process, and analyze data.

ยท Improve dataset quality and automate manual processes

ยท Provides insights and solutions that inform product team's business decisions

ยท Communicate proposals, findings with stakeholders and document through wiki for further consumption and distribution

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Recruitment Process and Others

  • Recruitment Process
  1. Application Review - Home Coding Test - Phone Interview - Virtual Onsite Interview โ€“ Offer
  2. The exact nature of the recruitment process may vary according to the specific job, and may be changed due to scheduling or other circumstances.
  3. Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage.

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index ๐Ÿ’ฐ

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Tags: A/B testing Airflow Bayesian CUDA CX Distributed Systems E-commerce Economics ETL Finance GPU KPIs Looker NLP Power BI Python Qlik R SAS Spark SQL Statistics STEM Superset Tableau

Region: Asia/Pacific
Country: South Korea

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