refresh

트렌딩 기업

트렌딩 기업

채용

채용Spotify

Data Scientist - Subscriptions

Spotify

Data Scientist - Subscriptions

Spotify

New York, NY

·

On-site

·

Full-time

·

3d ago

The Subscriptions team drives how Spotify grows by building compelling ways for listeners to experience and pay for Premium. We focus on creating the right plans, pricing, and value so that millions of listeners choose Spotify every day—and keep coming back.

You’ll join the Premium Desirability Data Science team within Subscriptions Product Insights. This team focuses on understanding what makes Spotify Premium valuable to listeners—and how we can make it even better.

We look at what drives people to subscribe, what keeps them engaged, and how we can continuously improve the Premium experience. You’ll work closely with Product, Engineering, Design, and Research to shape decisions that impact millions of users around the world.

총 조회수

0

총 지원 클릭 수

0

모의 지원자 수

0

스크랩

0

Spotify 소개

Spotify

Spotify

Public

Spotify is a Swedish audio streaming and media service provider founded in April 2006 by Daniel Ek and Martin Lorentzon. As of December 2025, it was one of the largest providers of music streaming services, with over 751 million monthly active users comprising 290 million paying subscribers.

5,001-10,000

직원 수

Stockholm

본사 위치

$23B

기업 가치

리뷰

4.1

10개 리뷰

워라밸

3.8

보상

4.2

문화

4.3

커리어

3.9

경영진

2.8

78%

친구에게 추천

장점

Flexible working hours and remote options

Great team culture and collaborative environment

Innovative projects and creative freedom

단점

High workload and performance pressure

Fast-paced environment can be overwhelming

Management issues and disorganization

연봉 정보

17개 데이터

Junior/L3

Mid/L4

Senior/L5

Staff/L6

Junior/L3 · Associate Data Scientist

0개 리포트

$142,458

총 연봉

기본급

-

주식

-

보너스

-

$121,089

$163,827

면접 경험

3개 면접

난이도

3.0

/ 5

소요 기간

14-28주

면접 과정

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

자주 나오는 질문

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit