Minimum qualifications:

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

Preferred qualifications:

• PhD degree in a quantitative field such as Engineering, Computer Science, Mathematics, Statistics, Economics, or Business Science.
• 5 years of work experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree.

About the job

As a Data Scientist in the Brand Ads Quality team, you will leverage eclectic quantitative repertoire, spanning operations research, game theory, statistics, and machine learning skills, to build industry-leading optimization solutions that will enable advertisers to divert a greater share of marketing budgets to YouTube where user engagement outpaces all other media channels. You will be included in tasks such as aiming relevance, creative recommendation, bidding optimization, and impact measurement. You will grow in an environment, proactively identify opportunities to enhance a business ecosystem, and showcase project ownership from solution design to engineering implementation and data-driven analyses culminating in production launch. You will partner with Software Engineers (SWEs) and Project Managers (PMs).

Responsibilities

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer and 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 and design and evaluate models to mathematically express and solve defined problems with limited precedent.
• Gather information, business goals, priorities, and organizational context around questions to answer and existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python); separately format, re-structure, or validate data to ensure quality; and review the dataset to ensure it is ready for analysis.