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职位Spotify

Data Scientist - Content Understanding

Spotify

Data Scientist - Content Understanding

Spotify

London

·

On-site

·

Full-time

·

1mo ago

必备技能

Machine Learning

Spotify’s consumer product is powered by an enormous and ever-growing catalog of music, podcasts, and audiobooks. The Content Platform team is responsible for making sure that content is ingested, structured, evaluated, and delivered reliably at global scale.

Our work ensures that creators’ content reaches fans quickly and accurately and that Spotify’s catalog is organized in a way that supports discovery, personalization, and a seamless listening experience. We partner closely with engineering, product, operations, labels, distributors, and research teams to keep our content ecosystem robust and scalable.

As a Data Scientist II, you’ll work at the intersection of user behavior, metadata, and machine learning systems to improve how content is classified, evaluated, and understood across Spotify.

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关于Spotify

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

员工数

Stockholm

总部位置

$23B

企业估值

评价

4.1

10条评价

工作生活平衡

3.8

薪酬

4.2

企业文化

4.3

职业发展

3.9

管理层

2.8

78%

推荐给朋友

优点

Flexible working hours and remote options

Great team culture and collaborative environment

Innovative projects and creative freedom

缺点

High workload and performance pressure

Fast-paced environment can be overwhelming

Management issues and disorganization

薪资范围

17个数据点

Junior/L3

Mid/L4

Senior/L5

Staff/L6

Junior/L3 · Associate Data Scientist

0份报告

$142,458

年薪总额

基本工资

-

股票

-

奖金

-

$121,089

$163,827

面试经验

3次面试

难度

3.0

/ 5

时长

14-28周

面试流程

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit