Coupang

Senior/Staff Data Analyst_Customer Experience Product Analytics

Seoul, South Korea
Python R SQL Hadoop Spark
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Description

 

Description

About Coupang ๐Ÿš€

์ฟ ํŒก์€ ๊ณ ๊ฐ์ด ์ฟ ํŒก ์•ฑ์„ ์—ด์–ด๋ณด๋Š” ์ˆœ๊ฐ„๋ถ€ํ„ฐ ์ƒํ’ˆ์„ ๋ฌธ ์•ž์œผ๋กœ ๋ฐฐ์†ก ๋ฐ›๋Š” ์ˆœ๊ฐ„๊นŒ์ง€, ๊ณ ๊ฐ ํ•œ ๋ถ„ ํ•œ ๋ถ„์—๊ฒŒ ๊ฐ๋™์„ ์ค„ ์ˆ˜ ์žˆ๋„๋ก ์‡ผํ•‘์˜ ๊ฒฝํ—˜์„ ์ƒˆ๋กญ๊ฒŒ ์ฐฝ์กฐํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋›ฐ์–ด๋‚œ ์—”๋“œํˆฌ์—”๋“œ ์ด์ปค๋จธ์Šค์™€ ๋ฌผ๋ฅ˜ ๋„คํŠธ์›Œํฌ, ๊ด‘์ ์œผ๋กœ ๊ณ ๊ฐ์—๊ฒŒ ์ง‘์ค‘ํ•˜๋Š” ๋ฌธํ™”๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฟ ํŒก์€ ์†๋„, ์…€๋ ‰์…˜, ๊ฐ€๊ฒฉ์— ์žˆ์–ด ํƒ€ํ˜‘ํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ํ˜„์žฌ ์ฟ ํŒก์€ ์‹ ์„  ์‹ํ’ˆ์„ ํฌํ•จํ•œ ์ˆ˜๋ฐฑ๋งŒ ์ข…๋ฅ˜์˜ ์ƒํ’ˆ์„ ์—„์ฒญ๋‚˜๊ฒŒ ๋น ๋ฅธ ์†๋„๋กœ ์—ฐ์ค‘๋ฌดํœด ๋ช‡ ์‹œ๊ฐ„ ์ด๋‚ด์— ์ „๊ตญ ๋ฐฐ์†กํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.

์ฟ ํŒก์€ ์„ธ๊ณ„์—์„œ ๊ฐ€์žฅ ํฌ๊ณ  ๋น ๋ฅด๊ฒŒ ์„ฑ์žฅํ•˜๋Š” ์‹œ์žฅ ์ค‘ ํ•˜๋‚˜์ธ ํ•œ๊ตญ์˜ ์ˆ˜๋ฐฑ๋งŒ ์†Œ๋น„์ž๋“ค์„ ์œ„ํ•ด ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ๊ณ ๊ฐ, ์ง์›, ํŒŒํŠธ๋„ˆ๋“ค์˜ ์ผ์ƒ์„ ์–ด๋–ป๊ฒŒ ํ˜์‹ ํ•  ์ˆ˜ ์žˆ์„์ง€ ๋งค ์ˆœ๊ฐ„ ๊ณ ๋ฏผํ•ฉ๋‹ˆ๋‹ค. ์•„์ง ์•„๋ฌด๋„ ํ’€์ง€ ๋ชปํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•จ์œผ๋กœ์จ ์‚ฌ๋žŒ๋“ค์ด โ€œ์ฟ ํŒก ์—†์ด ์–ด๋–ป๊ฒŒ ์‚ด์•˜์„๊นŒ?โ€๋ผ๊ณ  ๋ฌป๋Š” ์„ธ์ƒ์„ ๋งŒ๋“ค๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค. ์ฟ ํŒก์€ ์„œ์šธ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ฒ ์ด์ง•, LA, ์‹œ์• ํ‹€, ์ƒํ•˜์ด์™€ ์‹ค๋ฆฌ์ฝ˜๋ฐธ๋ฆฌ ๋“ฑ์— ์˜คํ”ผ์Šค๋ฅผ ๋‘๊ณ  ์žˆ๋Š” ๊ธ€๋กœ๋ฒŒ ๊ธฐ์—…์ž…๋‹ˆ๋‹ค.

Job Overview ๐Ÿš€

๋ฐ์ดํ„ฐ ๋ถ„์„๊ฐ€๋Š” ์ฟ ํŒก์ด ๋น„์ฆˆ๋‹ˆ์Šค ์˜์‚ฌ ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๋Š” ๋ฐ ํ•„์š”ํ•œ ๋ฐ์ดํ„ฐ์˜ ๋ถ„์„, ํ’ˆ์งˆ, ๊ฐ€์šฉ์„ฑ ๋ฐ๊นŠ์ด์™€ ๊ด€๋ จ๋œ ์—ฌ๋Ÿฌ ์˜ํ–ฅ๋ ฅ ์žˆ๋Š” ํ”„๋กœ์ ํŠธ๋ฅผ ์ด๋Œ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. SQL ๋ฐ ๊ธฐํƒ€ ์–ธ์–ด์— ๋Œ€ํ•œ ๊นŠ์€ ์ „๋ฌธ ์ง€์‹, ๋ฐ์ดํ„ฐ ETL ๊ฐœ๋ฐœ ๋ฐ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์ฟ ํŒก์˜ ๋ชจ๋“  ์ˆ˜์ค€์˜ ๋ถ„์„ ๋ฐ ์˜์‚ฌ๊ฒฐ์ •์— ํ•„์š”ํ•œ ์ •๋ณด๊ฐ€ ์ œ๊ณต๋˜๋„๋ก ํ•  ๊ฒƒ์ž…๋‹ˆ๋‹ค.

Customer Experience Product Analytics ํ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 ํŒก ๊ณ ๊ฐ์— ๋Œ€ํ•œ ๊นŠ์€ ์ดํ•ด๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ๋ถ„์„๊ฐ€๋Š” ๊ฒฝํ—˜์ ์ด๊ณ  ๋ฐ˜๋ฐ•ํ•  ์ˆ˜ ์—†๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ „๋žต์  ์ธ์‚ฌ์ดํŠธ๋ฅผ ํ†ตํ•ด ์ฟ ํŒก ํ”„๋กœ๊ทธ๋žจ์˜ ์‹คํ–‰, ์ „๋žต ๋ฐ ์ง„ํ™”๋ฅผ ๋ฆฌ๋“œํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.

๋˜ํ•œ Product Analytics ํŒ€์˜ ์ผ์›์œผ๋กœ์„œ PO์™€ ๊ธด๋ฐ€ํ•˜๊ฒŒ ํ˜‘์—…ํ•˜๋ฉฐ ์‚ฌ์šฉ์ž ๊ฒฝํ—˜์„ ์ตœ์ ํ™”ํ•ฉ๋‹ˆ๋‹ค. ์„ฑ๊ณต์ ์ธ A/B ํ…Œ์ŠคํŠธ์˜ ์‹คํ–‰ ๋ฐ ๋‹ค๋ณ€๋Ÿ‰ ํ…Œ์ŠคํŠธ ์ด๋‹ˆ์…”ํ‹ฐ๋ธŒ์™€ ํ•จ๊ป˜ ๋‹ค์–‘ํ•œ ๋ถ„์„ ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ํ”„๋กœ๋•ํŠธ KPI์˜ ๊ฐœ๋ฐœ ๋ฐ ์ตœ์ ํ™”๋ฅผ ์ฃผ๋„ํ•ฉ๋‹ˆ๋‹ค

Responsibilities ๐Ÿš€

ยท ๊ฐ€์„ค ๊ฒ€์ฆ : ๊ฐ€์„ค์„ ์„ธ์šฐ๊ณ  ๊ธฐํšŒ์— ๋Œ€ํ•ด ๊ฒ€์ฆ์„ ์‹ค์‹œํ•ฉ๋‹ˆ๋‹ค. ๋น„์ฆˆ๋‹ˆ์Šค KPI์™€ ๊ณ ๊ฐ๊ฒฝํ—˜์— ๊ธ์ •์  ๊ฐœ์„ ์„ ๋ถˆ๋Ÿฌ์˜ฌ ์•ก์…˜๋“ค์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค

ยท A/B ํ…Œ์ŠคํŠธ : ํ†ต๊ณ„์  ์—„๋ฐ€์„ฑ์„ ๊ฐ€์ง€๊ณ  A/B ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ํ…Œ์ŠคํŠธ ๊ฒฐ๊ณผ ๋™์ธ์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜๋Š” ์ฝ”ํ˜ธํŠธ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค. ๋ถ„์„์ ์ด๋ฉฐ ์ธ์‚ฌ์ดํŠธ๋ฅผ ์ œ๊ณตํ•˜๋Š” ๋ฆฌํฌํŒ… ๊ด€์ ์—์„œ ๋ณด๋‹ค ๋ณต์žกํ•œ ํ…Œ์ŠคํŠธ๋ฅผ ๊ตฌ์ƒํ•ฉ๋‹ˆ๋‹ค. ์ž๋™ํ™”๋œ ๋ฐ์ดํ„ฐ ์›Œํฌํ”Œ๋กœ์šฐ ๋ฐ ๋Œ€์‹œ๋ณด๋“œ๋ฅผ ๊ฐœ๋ฐœํ•˜์—ฌ ์ง€์†์ ์ธ ํ…Œ์ŠคํŠธ ์„ฑ๋Šฅ/๊ฒฐ๊ณผ๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•ฉ๋‹ˆ๋‹ค.

ยท ๊ฐ„๊ฒฐํ•œ ์Šคํ† ๋ฆฌ ์ „๋‹ฌ โ€“ ํ…Œ์ŠคํŠธ ์™„๋ฃŒ ์‹œ ์‹œ์˜์ ์ ˆํ•˜๊ณ  ์„ค๋“๋ ฅ์ด ์žˆ์œผ๋ฉฐ ์‚ฌ์‹ค์— ๊ธฐ๋ฐ˜ํ•œ ํ…Œ์ŠคํŠธ ๋‚ด์šฉ์„ ์ •๋ฆฌํ•ด ๋ฐ์ดํ„ฐ์™€ ํ…Œ์ŠคํŠธ ๊ฒฐ๊ณผ์— ์ˆจ๊ฒจ์ง„ ๋น„์ฆˆ๋‹ˆ์Šค โ€œ์Šคํ† ๋ฆฌ๋ฅผโ€ ๊ฐ•๋ ฅํžˆ ์ „๋‹ฌํ•ฉ๋‹ˆ๋‹ค.

ยท ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ - ์„ฑ๊ณต, ์‹คํŒจ, ์ถ”์„ธ๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ๊ฒฐ๊ณผ๋ฅผ ์กฐ์ง์— ํšจ๊ณผ์ ์œผ๋กœ ์ „๋‹ฌํ•ฉ๋‹ˆ๋‹ค. ๊ฒฐ๊ณผ๊ฐ€ ๊ธ์ •์ ์ด๋ฉด : A/B ํ…Œ์ŠคํŠธ ๋กค์•„์›ƒ ์‚ฌ์ „/์‚ฌํ›„์˜ KPI ๊ฐœ์„  ์‚ฌํ•ญ์„ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. / ๊ฒฐ๊ณผ๊ฐ€ ๋ถ€์ •์ ์ด๋ฉด : ๋™์ธ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ๋‹ค์Œ ํ”„๋กœ์ ํŠธ ์ดํ„ฐ๋ ˆ์ด์…˜์„ ์ถ”์ฒœํ•ฉ๋‹ˆ๋‹ค.

ยท ์ง๋ฌด์˜ Seniority์— ๋”ฐ๋ผCX ํ”„๋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˜ ์žˆ์œผ๋ฉฐ, ๋ฐ์ดํ„ฐ ๋ถ„์„๊ฐ€๋กœ ๊ตฌ์„ฑ๋œ ์ž‘์€ํŒ€(2-4์ธ)์„ ๊ด€๋ฆฌํ•˜๋Š” ๋ฆฌ๋”์‰ฝ ์—ญํ• ๊นŒ์ง€ ๊ธฐ๋Œ€ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.

Basic Qualifications ๐Ÿš€

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

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

ยท SQL/HQL ์ „๋ฌธ์ง€์‹๊ณผ ETL ๋ฐ dimensional modeling ๊ฒฝํ—˜

ยท Hadoop, Spark, Presto ๋“ฑ์˜ ๋น…๋ฐ์ดํ„ฐ ๊ธฐ์ˆ  ํ™œ์šฉ๋„

ยท Excel, Tableau, Power BI์™€ ๊ฐ™์€ ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ํˆด 5๋…„ ์ด์ƒ ์‚ฌ์šฉ ๊ฒฝํ—˜

ยท ๋ถ„์„์ ์ด๊ณ  ๋””ํ…Œ์ผ์— ๊ฐ•ํ•˜๋ฉฐ, ๋น„์ฆˆ๋‹ˆ์Šค ๊ฐ๊ฐ์ด ์žˆ์œผ์‹  ๋ถ„

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‰)

 

Preferred Experience ๐Ÿš€

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

ยท ํ†ต๊ณ„ ๋ถ„์„์„ ์œ„ํ•œ Python ๋˜๋Š” R/SAS ํ™œ์šฉ ์—ญ๋Ÿ‰

ยท Hive, Presto, Airflow

ยท ๊ธฐ์ˆ  ๋ถ„์•ผ ์„์‚ฌํ•™์œ„ ์†Œ์ง€์ž

ยท ํ†ต๊ณ„ ๋ถ„์•ผ ๊ฒฝํ—˜์ด ๋งŽ์€ ๋ถ„

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

ยท A/B ํ…Œ์ŠคํŒ… ๊ฒฝ๋ ฅ

[์ „ํ˜• ์ ˆ์ฐจ ๋ฐ ์•ˆ๋‚ด ์‚ฌํ•ญ]

  • ์ „ํ˜• ์ ˆ์ฐจ
    • ์„œ๋ฅ˜์ „ํ˜• - Home Test - ํ™”์ƒ๋ฉด์ ‘ (1.2์ฐจ) - ์ฒ˜์šฐํ˜‘์˜ - ์ตœ์ข…ํ•ฉ๊ฒฉ
    • ์ „ํ˜• ์ ˆ์ฐจ๋Š” ์ง๋ฌด ๋ณ„๋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‚ด๋“œ๋ฆฝ๋‹ˆ๋‹ค.
  • ์ฐธ๊ณ  ์‚ฌํ•ญ
    • ๋ณธ ๊ณต๊ณ ๋Š” ๋ชจ์ง‘ ์™„๋ฃŒ ์‹œ ์กฐ๊ธฐ ๋งˆ๊ฐ๋  ์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.
    • ์ง๊ธ‰๊ณผ ๋‹ด๋‹น ์—…๋ฌด ๋ฒ”์œ„๋Š” ํ›„๋ณด์ž์˜ ์ „๋ฐ˜์ ์ธ ๊ฒฝ๋ ฅ๊ณผ ๊ฒฝํ—˜ ๋“ฑ ์ œ๋ฐ˜ ์‚ฌ์ •์„ ๊ณ ๋ คํ•˜์—ฌ ๋ณ€๊ฒฝ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€๊ฒฝ์ด ํ•„์š”ํ•  ๊ฒฝ์šฐ, ์ตœ์ข… ํ•ฉ๊ฒฉ ํ†ต์ง€ ์ „ ์ ์ ˆํ•œ ์‹œ๊ธฐ์— ํ›„๋ณด์ž์™€ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ๋  ์˜ˆ์ •์ž…๋‹ˆ๋‹ค.
  • ๊ฐœ์ธ์ •๋ณด ์ฒ˜๋ฆฌ๋ฐฉ์นจ
    • ์ฟ ํŒก ๊ทธ๋ฃน์€ ์ž…์‚ฌ์ง€์›์ž ๊ฐœ์ธ์ •๋ณด ์ฒ˜๋ฆฌ๋ฐฉ์นจ(์•„๋ž˜ ๋งํฌ)์— ๋”ฐ๋ผ ๊ท€ํ•˜์˜ ๊ฐœ์ธ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ์ฒ˜๋ฆฌํ•ฉ๋‹ˆ๋‹ค. https://www.coupang.jobs/kr/privacy-policy/
  • ์„œ๋ฅ˜ ๋ฐ˜ํ™˜ ์ •์ฑ…
    • ๋ณธ ๊ณ ์ง€๋Š” ใ€Ž์ฑ„์šฉ์ ˆ์ฐจ์˜ ๊ณต์ •ํ™”์— ๊ด€ํ•œ ๋ฒ•๋ฅ ใ€ ์ œ11์กฐ ์ œ6ํ•ญ์— ๋”ฐ๋ฅธ ๊ฒƒ์ž…๋‹ˆ๋‹ค.
    • ๋‹น์‚ฌ ์ฑ„์šฉ์— ์‘์‹œํ•œ ๊ตฌ์ง์ž ์ค‘ ์ตœ์ข… ํ•ฉ๊ฒฉ์ด ๋˜์ง€ ๋ชปํ•œ ๊ตฌ์ง์ž๋Š” ใ€Ž์ฑ„์šฉ์ ˆ์ฐจ์˜ ๊ณต์ •ํ™”์— ๊ด€ํ•œ ๋ฒ•๋ฅ ใ€์— ๋”ฐ๋ผ ์ œ์ถœํ•œ ์ฑ„์šฉ์„œ๋ฅ˜์˜ ๋ฐ˜ํ™˜์„ ์ฒญ๊ตฌํ•  ์ˆ˜ ์žˆ์Œ์„ ์•Œ๋ ค ๋“œ๋ฆฝ๋‹ˆ๋‹ค. ๋‹ค๋งŒ, ํ™ˆํŽ˜์ด์ง€ ๋˜๋Š” ์ „์ž์šฐํŽธ์œผ๋กœ ์ œ์ถœ๋œ ๊ฒฝ์šฐ๋‚˜ ๊ตฌ์ง์ž๊ฐ€ ๋‹น์‚ฌ์˜ ์š”๊ตฌ ์—†์ด ์ž๋ฐœ์ ์œผ๋กœ ์ œ์ถœํ•œ ๊ฒฝ์šฐ์—๋Š” ๊ทธ๋Ÿฌํ•˜์ง€ ์•„๋‹ˆํ•˜๋ฉฐ, ์ฒœ์žฌ์ง€๋ณ€์ด๋‚˜ ๊ทธ ๋ฐ–์— ๋‹น์‚ฌ์—๊ฒŒ ์ฑ…์ž„ ์—†๋Š” ์‚ฌ์œ ๋กœ ์ฑ„์šฉ์„œ๋ฅ˜๊ฐ€ ๋ฉธ์‹ค๋œ ๊ฒฝ์šฐ์—๋Š” ๋ฐ˜ํ™˜ํ•œ ๊ฒƒ์œผ๋กœ ๋ด…๋‹ˆ๋‹ค.
    • ์œ„ 2ํ•ญ ๋ณธ๋ฌธ์— ๋”ฐ๋ผ ์ฑ„์šฉ์„œ๋ฅ˜ ๋ฐ˜ํ™˜ ์ฒญ๊ตฌ๋ฅผ ํ•˜๋Š” ๊ตฌ์ง์ž๋Š” ์ฑ„์šฉ์„œ๋ฅ˜ ๋ฐ˜ํ™˜์ฒญ๊ตฌ์„œ [์ฑ„์šฉ์ ˆ์ฐจ์˜ ๊ณต์ •ํ™”์— ๊ด€ํ•œ ๋ฒ•๋ฅ  ์‹œํ–‰๊ทœ์น™ ๋ณ„์ง€ ์ œ3ํ˜ธ ์„œ์‹]๋ฅผ ์ž‘์„ฑํ•˜์—ฌ ๋‹น์‚ฌ ์ฑ„์šฉํŒ€ (์„œ์šธ์‹œ ์†กํŒŒ๊ตฌ ์†กํŒŒ๋Œ€๋กœ 570 ํƒ€์›Œ730 ์ฟ ํŒก์ฑ„์šฉํŒ€) ์œผ๋กœ ์ œ์ถœํ•˜๋ฉด, ์ œ์ถœ์ด ํ™•์ธ๋œ ๋‚ ๋กœ๋ถ€ํ„ฐ 14์ผ ์ด๋‚ด์— ์ง€์ •ํ•œ ์ฃผ์†Œ์ง€๋กœ ๋“ฑ๊ธฐ์šฐํŽธ์„ ํ†ตํ•˜์—ฌ ๋ฐœ์†กํ•ด ๋“œ๋ฆฝ๋‹ˆ๋‹ค. ์ด ๊ฒฝ์šฐ ๋“ฑ๊ธฐ์šฐํŽธ์š”๊ธˆ์€ ์ˆ˜์‹ ์ž ๋ถ€๋‹ด์œผ๋กœ ํ•˜๊ฒŒ ๋˜์˜ค๋‹ˆ ์œ ๋…ํ•˜์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค.
    • ๋‹น์‚ฌ๋Š” ์œ„ 2ํ•ญ ๋ณธ๋ฌธ์— ๋”ฐ๋ฅธ ๊ตฌ์ง์ž์˜ ๋ฐ˜ํ™˜ ์ฒญ๊ตฌ์— ๋Œ€๋น„ํ•˜์—ฌ ์ฑ„์šฉ ์—ฌ๋ถ€๊ฐ€ ํ™•์ •๋œ ๋‚ ๋กœ๋ถ€ํ„ฐ 180์ผ๊ฐ„ ๊ตฌ์ง์ž๊ฐ€ ์ œ์ถœํ•œ ์ฑ„์šฉ์„œ๋ฅ˜ ์›๋ณธ์„ ๋ณด๊ด€ํ•˜๊ฒŒ ๋˜๋ฉฐ, ๊ทธ๋•Œ๊นŒ์ง€ ์ฑ„์šฉ์„œ๋ฅ˜์˜ ๋ฐ˜ํ™˜์„ ์ฒญ๊ตฌํ•˜์ง€ ์•„๋‹ˆํ•  ๊ฒฝ์šฐ์—๋Š” ใ€Ž๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ๋ฒ•ใ€์— ๋”ฐ๋ผ ์ง€์ฒด ์—†์ด ์ฑ„์šฉ์„œ๋ฅ˜ ์ผ์ฒด๋ฅผ ํŒŒ๊ธฐํ•  ์˜ˆ์ •์ž…๋‹ˆ๋‹ค.
    • ์ฑ„์šฉ ๋ฐ ์—…๋ฌด ์ˆ˜ํ–‰๊ณผ ๊ด€๋ จํ•˜์—ฌ ์š”๊ตฌ๋˜๋Š” ๋ฒ•๋ น์ƒ ์ž๊ฒฉ์ด ๊ฐ–์ถ”์–ด์ง€์ง€ ์•Š์€ ๊ฒฝ์šฐ ์ฑ„์šฉ์ด ์ œํ•œ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค

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.

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.

Qualifications :

ยท Wealth of experience in using SQL. Python(ideally using jupyter notebook) and Spark skill is a plus

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

ยท Having basic understanding of distributed systems, data modelling, and scientific methods. Proficient in descriptive statistics and familiar with 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

What you will do with us :

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

ยท For more senior roles (Staff Data Analysts I,II), we expect you to be able to own an analytical domain of increasing complexity within the CX products and independently engage with multiple POs to prioritise and drive analytical/decision science agenda, while managing a small team(2-4) of analysts.

[Recruitment Process and Others]

  • Recruitment Process:
    • Application Review - Home Test - Interview (2 rounds) - Offer
    • The recruitment process may be different depending on the job and may be changed due to scheduling and circumstances.
  • Others:
    • This job post may be closed early if all openings are filled.
    • If there is any false information in the application, the offer may be cancelled.
    • Veteran status or disability will not result in any disadvantages in the recruitment process.
    • Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage

[Privacy Notice]

 

Coupang
Coupang
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