Data Scientist, Ads Solutions Engineering

gTech Ads is responsible for all support and media and technical services for customers big and small across Google's entire Ad products stack. The team helps customers optimize their use of Ad and Publisher products, providing support ranging from self-help and in-product assistance to complex technical and measurement solutions for large customers. They collaborate with Sales, Product, and Engineering teams to develop better solutions, tools, and services to improve products and client experience. The Global Business Organization (GBO) powers Google's ecosystem of products and services to help customers and partners succeed and grow, including commercial arms of Ads sellers, business development, and customer support.

• Master's degree in a quantitative discipline such as Statistics, Engineering, or Sciences, or equivalent practical experience.
• 3 years of experience using analytics to solve product or business problems, including coding in Python, R, SQL, and querying databases or performing statistical analysis.

Nice to have:

• MBA or Master's degree in a related technical field.
• Experience with ad tech products such as ad servers, DSPs, DMPs, and web analytics, and understanding of engineering solutions built for scale.
• Experience in the ad space including first-party data onboarding and activation, measurement and data modeling, video and search advertising.
• Fluency in Mandarin and English to support client relationship management.
• Excellent project management, people management, leadership skills, and ability to formulate data-driven proposals.
• Excellent written and verbal business communication skills.

• Lead data science aspects of client engagements in marketing portfolio management.
• Collaborate with customers to resolve problems and identify suitable statistical techniques, owning the development of modeling frameworks.
• Engage stakeholders to assess data and model readiness and scale proof-of-concept solutions.
• Translate data and model results into tactical insights for decision-making and integrate recommendations into business processes.
• Collaborate with Product and Engineering teams to optimize Applied Data Science team capabilities, create opportunities for scale, and drive innovation.