Senior Research Data Scientist, Google Display Ads

Google•Mountain View, CA

2d•$174,000 - $252,000

About The Position

Google Display Ads (GDA) is a network of advertisers and publishers in which advertisers participate through auctions to win impressions (showing their ad to a user). Each advertiser has a business goal to achieve. That goal could be getting most conversions given their budget, most clicks given a limit on cost-per-click, or many other more advanced settings. Our larger team is responsible for the backend systems that power all these optimizations. Specifically for our team, we work on GDA Ads retrieval, where the core objective is to efficiently find the ad candidate that can deliver the highest value for a given query, a difficult task since Google has access to millions of ads. We need to build an efficient system to score, rank and select candidates at large scale, and send them to the final auction to get more accurate bidding and prediction. We provide a framework that dynamically controls the trade-off between revenue and resource usage by automatically adjusting serving parameters on a per-query basis. Our goal is to maximize advertiser value (revenue) subject to constraints on serving resources and latency. You will be working on a system that directly impacts the efficiency and profitability of a large-scale advertising platform.

Requirements

Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
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 3 years of work experience with a PhD degree.

Nice To Haves

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.

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 relevant tools (e.g., SQL, R, Python). Independently format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.