About the job

gTech Ads is responsible for all support and media and technical services for customers big and small across our entire Ad products stack. We help our customers get the most out of our Ad and Publisher products and guide them when they need help. We provide a range of services from enabling better self help and in-product support, to providing better support through interactions, setting up accounts and implementing ad campaigns, and providing media solutions for customers business and marketing needs and providing complex technical and measurement solutions along with consultative support for our large customers. These solutions range from bespoke and customized ones for our customers to scalable support for millions of customers worldwide. Based on the evolving needs of our ads customers, we partner with Sales, Product and Engineering teams within Google to develop better solutions, tools, and services to improve our products and enhance our client experience. As a cross-functional and global team, we ensure our customers get the best return on investment with Google and we remain a trusted partner.

The Google Customer Solutions Partner Engineering team within gTech Ads is made up of client-facing engineers that deliver custom solutions and technical consulting to Google's advertisers, advertising agencies, and other partners. We anticipate, understand and solve business challenges by tailoring innovative solutions in a timely fashion. The Data scientist role is unique as it combines consulting skills with data science skills, requireing strong cross-functional operations. In return, the role provides the ability to achieve large and tangible impact and the ability to integrate with a large business team and large growing partners.

Responsibilities

• Lead data science aspects of client engagements in the area of marketing portfolio management.
• Collaborate with customers to resolve their problems and identify the best statistical techniques that can solve the problem, and own the development of a modeling framework.
• Engage important stakeholders to assess data and model readiness and be able to scale a proof-of-concept to a solution.
• Work with customer and internal teams to translate data and model results into tactical and strategic insights for decision-making, and work with clients to integrate recommendations into business processes.
• Collaborate with Product/Engineering teams to increase and optimize capabilities of the Applied Data Science (DS) team, employing methods which create opportunities for scale, and help to drive innovation.