Data Science Manager - TikTok Ads

Responsibilities

We're the TikTok Monetization Products data science team, who enables and champions data driven decision making. Our Vision is to become the world class data science team, where data is used rigorously to drive all decision making. Our Mission is to drive monetization and sustainable revenue growth for TikTok through data science.

Responsibilities:
1. Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
2. Build and manage high performance, responsive team of data scientists and analysts that are able to not only keep up with but also pioneer in this space
3. Build and prototype analysis pipelines for the team to provide insights at scale
4. Develop comprehensive knowledge of TikTok data structures and metrics, advocating for changes where needed for product development
5. Develop deep partnerships with engineer and product teams to deliver on major cross-functional measurements, testing, and modeling efforts
6. Partner with Data Engineering to ensure the data accuracy and consistency

Qualifications

Minimum Qualifications:
1. MS or PhD in a quantitative field such as Computer Science, Engineering, Math, Statistics, etc.
2. many years of experience in data science, algorithmic engineering, or machine learning
3. 3+ years of experience in managing and mentoring data science, data engineering, and analytics teams in a technology company
4. Experience with statistical software (e.g. R, Python, MATLAB) and database languages (e.g. SQL)
5. Experience building data science models (Regression, Decision Trees, K-Means, etc.)

Preferred Qualifications:
1. Experience with large data sets and analytical tools, e.g. Hive, Spark
2. Demonstrated leadership and self-direction. Willingness to both teach others and learn new techniques.
3. Demonstrated skills in selecting the right statistical tools given a data analysis problem.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $242060 - $387600 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

Responsibilities

Responsibilities

We're the TikTok Monetization Products data science team, who enables and champions data driven decision making. Our Vision is to become the world class data science team, where data is used rigorously to drive all decision making. Our Mission is to drive monetization and sustainable revenue growth for TikTok through data science.

Responsibilities:
1. Work with large, complex data sets. Solve difficult, non-routine analysis problems, applying advanced analytical methods as needed. Conduct analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.
2. Build and manage high performance, responsive team of data scientists and analysts that are able to not only keep up with but also pioneer in this space
3. Build and prototype analysis pipelines for the team to provide insights at scale
4. Develop comprehensive knowledge of TikTok data structures and metrics, advocating for changes where needed for product development
5. Develop deep partnerships with engineer and product teams to deliver on major cross-functional measurements, testing, and modeling efforts
6. Partner with Data Engineering to ensure the data accuracy and consistency

Qualifications

Qualifications

Minimum Qualifications:
1. MS or PhD in a quantitative field such as Computer Science, Engineering, Math, Statistics, etc.
2. many years of experience in data science, algorithmic engineering, or machine learning
3. 3+ years of experience in managing and mentoring data science, data engineering, and analytics teams in a technology company
4. Experience with statistical software (e.g. R, Python, MATLAB) and database languages (e.g. SQL)
5. Experience building data science models (Regression, Decision Trees, K-Means, etc.)

Preferred Qualifications:
1. Experience with large data sets and analytical tools, e.g. Hive, Spark
2. Demonstrated leadership and self-direction. Willingness to both teach others and learn new techniques.
3. Demonstrated skills in selecting the right statistical tools given a data analysis problem.