Growth-Focused Lead Data Scientist (User Growth)

TikTok; San Jose (CA); On-site; USD 219,000 - 408,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) with company match

Parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

10 paid holidays, 10 paid sick days, 7

Job summary

TikTok is seeking a Lead Data Scientist for User Growth in San Jose. You will work with product managers, engineers, and data analysts to drive growth, design models, and guide decisions with causal analyses.

You will lead model development, oversee MLOps, and shape the analytics roadmap for user growth initiatives, with cross-functional collaboration and a strong emphasis on quantitative rigor.

Qualifications

• Master's degree in a quantitative discipline.
• 5+ years of experience in statistical modeling (Regression, Classification, Clustering, etc.) and machine learning (deep learning).
• 2+ years of experience with leading a team.
• Proficiency in SQL, Python/R.
• Strong communication and collaboration skills.

Responsibilities

• Analyze product usage and performance data to inform decisions for business leaders.
• Develop and improve models such as LTV and ROI to guide growth decisions.
• Conduct model-driven analyses and establish inter-product analytic paradigms.
• Collaborate with Product, Operations, Engineering and cross-functional teams to drive development.
• Design, run and analyze A/B experiments to inform product strategy.

Skills

Statistical modeling

Machine learning

Leadership

Communication

Data analysis

Education

Master’s degree in quantitative discipline

Tools

SQL

Python

R

Job description

TikTok is seeking a Lead Data Scientist for User Growth in San Jose. You will work with product managers, engineers, and data analysts to drive growth, design models, and guide decisions with causal analyses.

You will lead model development, oversee MLOps, and shape the analytics roadmap for user growth initiatives, with cross-functional collaboration and a strong emphasis on quantitative rigor.