Coupang
4 weeks ago

Senior/Staff Data Analyst_CX Product Analytics

Seoul, South Korea
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€,ย ๊ณ ๊ฐ ํ•œ ๋ถ„ ํ•œ ๋ถ„์—๊ฒŒ ๊ฐ๋™์„ ์ค„ ์ˆ˜ ์žˆ๋„๋ก ์‡ผํ•‘์˜ ๊ฒฝํ—˜์„ ์ƒˆ๋กญ๊ฒŒ ์ฐฝ์กฐํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.ย ๋›ฐ์–ด๋‚œ ์—”๋“œํˆฌ์—”๋“œ ์ด์ปค๋จธ์Šค์™€ ๋ฌผ๋ฅ˜ ๋„คํŠธ์›Œํฌ,ย ๊ด‘์ ์œผ๋กœ ๊ณ ๊ฐ์—๊ฒŒ ์ง‘์ค‘ํ•˜๋Š” ๋ฌธํ™”๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ฟ ํŒก์€ ์†๋„,ย ์…€๋ ‰์…˜,ย ๊ฐ€๊ฒฉ์— ์žˆ์–ด ํƒ€ํ˜‘ํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค.ย ํ˜„์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.ย ์šฐ๋ฆฌ๋Š” ๊ณ ๊ฐ,ย ์ง์›,ย ํŒŒํŠธ๋„ˆ๋“ค์˜ ์ผ์ƒ์„ ์–ด๋–ป๊ฒŒ ํ˜์‹ ํ•  ์ˆ˜ ์žˆ์„์ง€ ๋งค ์ˆœ๊ฐ„ ๊ณ ๋ฏผํ•ฉ๋‹ˆ๋‹ค.ย ์•„์ง ์•„๋ฌด๋„ ํ’€์ง€ ๋ชปํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•จ์œผ๋กœ์จ ์‚ฌ๋žŒ๋“ค์ดย โ€œ์ฟ ํŒก ์—†์ด ์–ด๋–ป๊ฒŒ ์‚ด์•˜์„๊นŒ?โ€๋ผ๊ณ  ๋ฌป๋Š” ์„ธ์ƒ์„ ๋งŒ๋“ค๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค.ย ์ฟ ํŒก์€ ์„œ์šธ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ฒ ์ด์ง•, LA,ย ์‹œ์• ํ‹€,ย ์ƒํ•˜์ด์™€ ์‹ค๋ฆฌ์ฝ˜๋ฐธ๋ฆฌ ๋“ฑ์— ์˜คํ”ผ์Šค๋ฅผ ๋‘๊ณ  ์žˆ๋Š” ๊ธ€๋กœ๋ฒŒ ๊ธฐ์—…์ž…๋‹ˆ๋‹ค.
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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„์„,ย ํ’ˆ์งˆ,ย ๊ฐ€์šฉ์„ฑ ๋ฐ๊นŠ์ด์™€ ๊ด€๋ จ๋œ ์—ฌ๋Ÿฌ ์˜ํ–ฅ๋ ฅ ์žˆ๋Š” ํ”„๋กœ์ ํŠธ๋ฅผ ์ด๋Œ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. SQLย ๋ฐ ๊ธฐํƒ€ ์–ธ์–ด์— ๋Œ€ํ•œ ๊นŠ์€ ์ „๋ฌธ ์ง€์‹,ย ๋ฐ์ดํ„ฐย ETLย ๊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 Analyticsย ํŒ€์€ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ฉํ•˜์—ฌ ์‹คํ–‰ ๊ฐ€๋Šฅํ•œ ์ธ์‚ฌ์ดํŠธ๋ฅผ ์ƒ์„ฑํ•˜๊ณ ,ย ๋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 ํŒกย ย ํ”„๋กœ๊ทธ๋žจ์˜ ์‹คํ–‰,ย ์ „๋žต ๋ฐ ์ง„ํ™”๋ฅผ ๋ฆฌ๋“œํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
๋˜ํ•œย Product Analyticsย ํŒ€์˜ ์ผ์›์œผ๋กœ์„œย PO์™€ ๊ธด๋ฐ€ํ•˜๊ฒŒ ํ˜‘์—…ํ•˜๋ฉฐ ์‚ฌ์šฉ์ž ๊ฒฝํ—˜์„ ์ตœ์ ํ™”ํ•ฉ๋‹ˆ๋‹ค.ย ์„ฑ๊ณต์ ์ธย A/Bย ํ…Œ์ŠคํŠธ์˜ ์‹คํ–‰ ๋ฐ ๋‹ค๋ณ€๋Ÿ‰ ํ…Œ์ŠคํŠธ ์ด๋‹ˆ์…”ํ‹ฐ๋ธŒ์™€ ํ•จ๊ป˜ ๋‹ค์–‘ํ•œ ๋ถ„์„ ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ํ”„๋กœ๋•ํŠธย KPI์˜ ๊ฐœ๋ฐœ ๋ฐ ์ตœ์ ํ™”๋ฅผ ์ฃผ๋„ํ•ฉ๋‹ˆ๋‹ค
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Responsibilities ๐Ÿš€
ยทย ย ย ย ย  ย ๊ฐ€์„ค ๊ฒ€์ฆย :ย ๊ฐ€์„ค์„ ์„ธ์šฐ๊ณ  ๊ธฐํšŒ์— ๋Œ€ํ•ด ๊ฒ€์ฆ์„ ์‹ค์‹œํ•ฉ๋‹ˆ๋‹ค.ย ๋น„์ฆˆ๋‹ˆ์Šคย KPI์™€ ๊ณ ๊ฐ๊ฒฝํ—˜์— ๊ธ์ •์  ๊ฐœ์„ ์„ ๋ถˆ๋Ÿฌ์˜ฌ ์•ก์…˜๋“ค์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค
ยทย ย ย ย ย  ย ์ปดํ”Œ๋ผ์ด์–ธ์Šค ๋ฐ ๋ฒ•๋ฌด ํŒ€๊ณผ ์ง์ ‘ ํ˜‘์—…ํ•˜์—ฌ ๋ฐ์ดํ„ฐ ๋ถ„์„ ์š”๊ตฌ ์‚ฌํ•ญ์„ ํŒŒ์•…ํ•˜๊ณ  ์ ์‹œ์— ์ •ํ™•ํ•˜๊ฒŒ ๋Œ€์‘ํ•ฉ๋‹ˆ๋‹ค.
ยทย ย ย ย ย  ย A/Bย ํ…Œ์ŠคํŠธย :ย ํ†ต๊ณ„์  ์—„๋ฐ€์„ฑ์„ ๊ฐ€์ง€๊ณ ย A/Bย ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ณ ,ย ํ…Œ์ŠคํŠธ ๊ฒฐ๊ณผ ๋™์ธ์— ๋Œ€ํ•ด ๋ถ„์„ํ•˜๋Š” ์ฝ”ํ˜ธํŠธ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค.ย ๋ถ„์„์ ์ด๋ฉฐ ์ธ์‚ฌ์ดํŠธ๋ฅผ ์ œ๊ณตํ•˜๋Š” ๋ฆฌํฌํŒ… ๊ด€์ ์—์„œ ๋ณด๋‹ค ๋ณต์žกํ•œ ํ…Œ์ŠคํŠธ๋ฅผ ๊ตฌ์ƒํ•ฉ๋‹ˆ๋‹ค.ย ์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.
ยทย ย ย ย ย  ย ๊ฐ„๊ฒฐํ•œ ์Šคํ† ๋ฆฌ ์ „๋‹ฌย โ€“ย ํ…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‹ˆ์Šคย โ€œ์Šคํ† ๋ฆฌ๋ฅผโ€ย ๊ฐ•๋ ฅํžˆ ์ „๋‹ฌํ•ฉ๋‹ˆ๋‹ค.
ยทย ย ย ย ย  ย ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ย -ย ์„ฑ๊ณต,ย ์‹คํŒจ,ย ์ถ”์„ธ๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ๊ฒฐ๊ณผ๋ฅผ ์กฐ์ง์— ํšจ๊ณผ์ ์œผ๋กœ ์ „๋‹ฌํ•ฉ๋‹ˆ๋‹ค.ย ๊ฒฐ๊ณผ๊ฐ€ ๊ธ์ •์ ์ด๋ฉดย : A/Bย ํ…Œ์ŠคํŠธ ๋กค์•„์›ƒ ์‚ฌ์ „/์‚ฌํ›„์˜ย KPIย ๊ฐœ์„  ์‚ฌํ•ญ์„ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. /ย ๊ฒฐ๊ณผ๊ฐ€ ๋ถ€์ •์ ์ด๋ฉดย :ย ๋™์ธ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ๋‹ค์Œ ํ”„๋กœ์ ํŠธ ์ดํ„ฐ๋ ˆ์ด์…˜์„ ์ถ”์ฒœํ•ฉ๋‹ˆ๋‹ค
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Basic Qualifications ๐Ÿš€
ยทย ย ย ย ย  ย ์ •๋Ÿ‰๋ถ„์•ผ์˜ ํ•™์‚ฌย (STEM, Finance, Economics, Statistics)
ยทย  ย  ย  ย ๋ฐ์ดํ„ฐ ์„ธ๋ถ€ ์‚ฌํ•ญ, ์ •ํ™•์„ฑ ๋ฐ ๋ฌด๊ฒฐ์„ฑ์— ๋Œ€ํ•œ ๋†’์€ ์ˆ˜์ค€์˜ ์—ญ๋Ÿ‰
ยทย ย ย ย ย  ย Businessย Analyst,ย Dataย Analyst, FP&A,ย Dataย Scientistย ๋“ฑ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ธฐ์ˆ ์„ ํ•„์š”๋กœํ•˜๋Š” ์—…๋ฌด ์•ฝย 3๋…„ ์ด์ƒ์˜ ๊ฒฝํ—˜
ยทย ย ย ย ย  ย SQL/HQLย ์ „๋ฌธ์ง€์‹๊ณผย ETLย ๋ฐย dimensional modelingย ๊ฒฝํ—˜
ยทย ย ย ย ย  ย Hadoop, Spark, Prestoย ๋“ฑ์˜ ๋น…๋ฐ์ดํ„ฐ ๊ธฐ์ˆ  ํ™œ์šฉ๋„
ยทย ย ย ย ย  ย Excel, Tableau, Power BI์™€ ๊ฐ™์€ ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™” ํˆดย 5๋…„ ์ด์ƒ ์‚ฌ์šฉ ๊ฒฝํ—˜
ยทย ย ย ย ย  ย ๋ถ„์„์ ์ด๊ณ  ๋””ํ…Œ์ผ์— ๊ฐ•ํ•˜๋ฉฐ,ย ๋น„์ฆˆ๋‹ˆ์Šค ๊ฐ๊ฐ์ด ์žˆ์œผ์‹  ๋ถ„
ยทย ย ย ย ย  ย ์—…๋ฌด์˜ ์šฐ์„  ์ˆœ์œ„๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์ •ํ•˜๊ณ ,ย ๋น ๋ฅด๊ณ  ์—ญ๋™์ ์ธ ํ™˜๊ฒฝ์—์„œ ํšจ๊ณผ์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์—ญ๋Ÿ‰ย (๋ฉ€ํ‹ฐํ…Œ์Šคํ‚น ์—ญ๋Ÿ‰)
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Preferred Experience ๐Ÿš€
ยทย ย ย ย ย  ย ๊ณตํ•™ ๋ฐ ๋น„์ง€๋‹ˆ์Šค ํ•™์œ„ย (์„/๋ฐ•์‚ฌย or MBA)
ยทย ย ย ย ย  ย ํ†ต๊ณ„ ๋ถ„์„์„ ์œ„ํ•œย Pythonย ๋˜๋Š”ย R/SASย ํ™œ์šฉ ์—ญ๋Ÿ‰
ยทย ย ย ย ย  ย Hive, Presto, Airflow
ยทย ย ย ย ย  ย ๊ธฐ์ˆ  ๋ถ„์•ผ ์„์‚ฌํ•™์œ„ ์†Œ์ง€์ž
ยทย ย ย ย ย  ย ํ†ต๊ณ„ ๋ถ„์•ผ ๊ฒฝํ—˜์ด ๋งŽ์€ ๋ถ„
ยทย ย ย ย ย  ย ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”ย (์˜ˆ: Tableau, Qlik, Looker, Power BI)
ยทย ย ย ย ย  ย A/Bย ํ…Œ์ŠคํŒ… ๊ฒฝ๋ ฅ

์ „ํ˜• ์ ˆ์ฐจย ๋ฐย ์•ˆ๋‚ดย ์‚ฌํ•ญ

์ „ํ˜•์ ˆ์ฐจ

  • ์„œ๋ฅ˜์ „ํ˜• - ์ „ํ™”๋ฉด์ ‘ - ๋น„๋Œ€๋ฉด(ํ™”์ƒ)๋ฉด์ ‘ โ€“ ์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‚ด ๋“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.
  • ์ทจ์—… ๋ณดํ˜ธ ๋Œ€์ƒ์ž(๋ณดํ›ˆ๋Œ€์ƒ์ž, ์žฅ์• ์ธ ๋“ฑ)๋Š” ๊ด€๋ จ ๋ฒ•๋ฅ ์— ๋”ฐ๋ผ ์ฑ„์šฉ์šฐ๋Œ€๋ฅผ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹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 •๋ณดย ์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/

ย 
Customer Experience Product Analytics and Decision Science (CX PA & DS) / Seoul, Korea
ย 
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.
ย 
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.
ย 
Qualifications :
ยท Wealth of experience in using SQL. Python(ideally using jupyter notebook) and Spark skill is a plus
ยทย High level of attention to detail and a commitment to data accuracy and integrity
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,ย dataย modelling, and scientific methods. Proficient in descriptive statistics and familiar with inferential statistics
ยท Good presentation and communication skills in explainingย data, as we often engage non-dataย 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
ย 
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 Collaborate directly with Compliance and Legal teams to understand their dataย analysis needs and respond in a timely and accurate manner
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 automatedย dataย workflows and dashboards to monitor ongoing test performance/results, with maintaining keyย dataย artifacts and lineage (e.g., ETL,ย dataย models, queries)
ยท Utilizes relevant visualization tools (Tableau/PowerBI/Superset/etc) to help track metrics and investigateย dataย 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 analyzeย data.
ยท Improveย 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 moreย seniorย roles (Staffย Dataย 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) ofย analysts.

ย 

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