Data Scientist Tech Lead, YouTube Ads

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
  
• 8 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
  

Preferred qualifications:

• 10 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 8 years of work experience with a PhD degree.
  
• 3 years of experience as a people manager within a technical leadership role.
  
• Experience with Machine Learning, Game Theory.

About the job

In this role, you will serve as a Technical Lead (TL) for a hybrid team of Data Scientists and Software Engineers to grow the YouTube Ads business by optimizing YouTube's marketplace. You will work with a team of ML engineers, Software engineers, Data Scientists and Product Managers, to study user ad engagement behavior that aligns with advertiser objectives and will model the impact of ads on users short term and long term behavior. You will leverage these models to optimize bidding and pricing, so to ensure optimal combination of advertiser performance and user experience.The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into tractable analysis, evaluation metrics, or mathematical models.
  
• Use custom data infrastructure or existing data models as appropriate, using specialized knowledge. Design and evaluate models to mathematically express and solve defined problems with limited precedent.
  
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
  
• Own the process of gathering, extracting, and compiling data across sources via tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
• Formulate advanced statistical and machine learning models, distributed data processing pipelines.