Data Scientist, PGC Traffic & Creator Growth

TikTok; San Jose (CA); On-site; USD 179,000 - 277,000; Full time

Job summary

TikTok is seeking a Data Scientist to analyze user behavior, product features, and the content ecosystem to generate actionable business insights. You will partner with Product Management, Research and Development, and Machine Learning Engineering to improve user experience and support growth across regions.

You will collaborate closely with ML engineers to enhance recommendation systems, assess creator rewards, and develop experimental methodologies for two-sided market dynamics.

Qualifications

• Bachelor's degree in data science, statistics, econometrics, math or a related scientific major, with 3+ years of related work experience.
• Expertise in SQL and programming in Python or R.
• Excellent analysis skills with good communication skills.
• Strong logical and quantitative skills, with practical experience in recommendation systems.

Responsibilities

• Collaborate with MLE to enhance the efficiency of the recommendation system and improve user experience.
• Utilize data science methods to assess the creator rewards program’s progress and develop new strategies.
• Develop experimental methodologies to evaluate the impact of two-sided market dynamics.
• Create a metrics framework to monitor product health, track core metrics, and identify the root causes of metric changes.
• Utilize causal inference and discovery to evaluate causality in non-experimental contexts.

Skills

SQL

Python

R

Data analysis

Recommendation systems

Education

Bachelor's degree in data science, statistics, econometrics, math or related major

Job description

TikTok is seeking a Data Scientist to analyze user behavior, product features, and the content ecosystem to generate actionable business insights. You will partner with Product Management, Research and Development, and Machine Learning Engineering to improve user experience and support growth across regions.

You will collaborate closely with ML engineers to enhance recommendation systems, assess creator rewards, and develop experimental methodologies for two-sided market dynamics.