Research Data Scientist, YouTube Ads Bidding

Google•Mountain View, CA

5d•Onsite

About The Position

We are a YouTube ads bidding team, our team's mission is to make every advertiser successful on YouTube. The team's focus area includes building reliable and scalable offline and serving infrastructure, innovating new direct response products, identifying and eliminating sources of poor performance, and solving bidding optimization problems. In this role, you will have effective investigative skills to identify problems and solutions, as well as strong statistical modeling skills to define standard metrics and models to be used in bidding. Our projects are a mixture of engineering, machine learning and analytical work. Users come first at Google. Nowhere is this more important than on our Advertising and Commerce team: we believe that ads and commercial information can be highly useful to our users if that information is relevant to what our users wish to find or do. Advertisers worldwide use Google Ads to promote their products; publishers use AdSense to serve relevant ads on their website; and business around the world use our products (like Google Shopping, and Google Wallet) to support their online businesses and bring users into their offline stores. We are constantly innovating to deliver the most effective advertising and commerce opportunities of tomorrow.

Requirements

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
3 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.

Nice To Haves

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.

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.
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 relevant 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.