Business Data Scientist, gTech Ads

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.

• Lead data science aspects of client engagements in the area of marketing effectiveness and marketing portfolio management with Machine Learning and statistics.
• Collaborate with customers to unpack their problems and identify the best statistical techniques that can solve the problem; 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 larger solution, collaborate with Product/Engineering teams to increase and optimize capabilities of our data scientist (DS) team, employing methods which create opportunities for scale, proactively helping to drive innovation.
• Work with customer and internal teams to translate data and model results into tactical and insights that are actionable for decision.
• Develop comprehensive understanding of Google data structures, and metrics, advocating for product changes where needed.

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
• 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
• Experience in Machine Learning and Statistics.
• Experience delivering insights from ML to customers (e.g., problem scoping/definition, modeling, people-management).
• Experience using or deploying digital analytics and measurement solutions.
• Experience in retail, financial services/retail banking, insurance, telecommunications, automotive and CPG.
• Experience in Computer Vision and Natural Language Processing (NLP) in the context of marketing analytics and the ability to bring generative AI technologies to customer problems in marketing.
• Experience leveraging data insights into storytelling for business stakeholders.
• Understanding of the statistical algorithms typically used in Marketing Analytics.

• Experience delivering insights from ML to customers (e.g., problem scoping/definition, modeling, people-management).
• Experience using or deploying digital analytics and measurement solutions.
• Experience in retail, financial services/retail banking, insurance, telecommunications, automotive and CPG.
• Experience in Computer Vision and Natural Language Processing (NLP) in the context of marketing analytics and the ability to bring generative AI technologies to customer problems in marketing.
• Experience leveraging data insights into storytelling for business stakeholders.
• Understanding of the statistical algorithms typically used in Marketing Analytics.

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