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Data Scientist Intern (E-commerce Governance Algorithms) - 2025 Summer/Fall (BS/MS)

TikTok

Data Scientist Intern (E-commerce Governance Algorithms) - 2025 Summer/Fall (BS/MS)

TikTok

San Jose, CA

·

On-site

·

Full-time

·

2mo ago

복지 및 혜택

Parental Leave

Healthcare

Equity

Unlimited Pto

필수 스킬

Airflow

SQL

Python

Responsibilities

Governance and Experience team of e-commerce are responsible for ensuring the safety and trust-worth of the marketplace for not only our users, but also sellers and creators. We value our users' satisfaction by working on policies, rules and systems to ensure the quality. We are looking for an algorithm technical manager who is obsessed with customer experience and passionate about building systems to detect various risks that can sabotage platform trust.

We are looking for talented individuals to join us for an internship in 2025. Internships at Tik Tok aim to offer students industry exposure and hands-on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at Tik Tok

Internships at Tik Tok aim to provide students with hands-on experience in developing fundamental skills and exploring potential career paths. A vibrant blend of social events and enriching development workshops will be available for you to explore. Here, you will utilize your knowledge in real-world scenarios while laying a strong foundation for personal and professional growth. It runs for 12 weeks beginning in May/June 2025 or August/September 2025 (Select May if Summer or August if Fall

Please state your availability clearly in your resume (Start date, End date).

Summer Start Dates:

  • Monday, May 12
  • Monday, May 19
  • Tuesday May 27 (Memorial Day May 26)
  • Monday, June 9
  • Monday, June 23

Fall Start Dates:

  • Monday, August 11
  • Monday, August 25
  • Monday, September 8
  • Monday, September 22

Applications will be reviewed on a rolling basis. We encourage you to apply early. Candidates can apply to a maximum of TWO positions and will be considered for jobs in the order you apply. The application limit is applicable to Tik Tok and its affiliates' jobs globally.

Candidates who pass resume evaluation will be invited to participate in Tik Tok's technical online assessment through Hacker Rank.

Key Responsibilities:

  • Develop scientific metrics to measure success of E-Commerce governance and experience team
  • Develop the credit system for merchants and creators to incentivize good behavior
  • Work closely with engineers and product managers, and drive project execution and iteration by clear problem scoping and design

Qualifications

Minimum Qualifications:

  • Currently pursuing a BS/MS degree in Computer Science, Statistics, Economy or other relevant majors with 2+ years of work experience
  • Proficient in at least one software programming language, such as SQL/R/Python
  • Good self-driving ability and team cooperation ability, strong sense of responsibility, willingness and ability to challenge oneself and learn new knowledge

Preferred Qualifications:

  • Experiences in Hadoop/Spark/Storm and other computing platforms and tools
  • Hands-on experience in open source machine learning frameworks such as pytorch and tensorflow

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모의 지원자 수

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TikTok 소개

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

직원 수

Los Angeles

본사 위치

$220B

기업 가치

리뷰

3.8

10개 리뷰

워라밸

2.8

보상

3.7

문화

4.1

커리어

3.2

경영진

2.9

68%

친구에게 추천

장점

Great team dynamics and support

Innovative and creative culture

Good learning opportunities

단점

Work-life balance challenges

Fast-paced and stressful environment

High expectations and tight deadlines

연봉 정보

49개 데이터

Mid/L4

Senior/L5

Mid/L4 · Applied AI Product Data Scientist

1개 리포트

$273,000

총 연봉

기본급

$210,000

주식

-

보너스

-

$273,000

$273,000

면접 경험

2개 면접

난이도

4.0

/ 5

소요 기간

21-35주

경험

긍정 0%

보통 0%

부정 100%

면접 과정

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Behavioral Interview

5

Final Round

6

Offer

자주 나오는 질문

Coding/Algorithm

Behavioral/STAR

Technical Knowledge

Culture Fit