In this role, you will be responsible for applying best data science practices to solve our ads customers problems in marketing. You will be passionate about bringing the best of Googles Machine Learning (ML) capabilities to build innovative solutions that address our customer's problem-space. You will be responsible for executing on gTechs strategy and goal to deliver applied data science to Google's largest ads customers.
The Business Operations and Strategy team at Google plays a critical role in defining and driving strategic, operational and organizational improvements across the company. Also known as "BizOps", the group operates like an internal consulting group working on a range of critical projects and issues. BizOps creates strategies for promoting growth of our products like YouTube, Chrome and Mobile. They handle issues around partner development, strategy work in emerging markets such as Africa and India, as well as pricing strategies across our B2B and consumer products. The BizOps team is at the forefront of Google's fast-moving strategic priorities, addressing operational challenges and enabling innovation.
Responsibilities
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.
Requirements: Minimum qualifications:
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.
Preferred qualifications:
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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