Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 3 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.
  

Preferred qualifications:

• Master's degree in Statistics, Mathematics, Bioinformatics, Economics, another quantitative field, or equivalent practical experience.
  
• 2 years of experience in a data science, or a related technical field.
  
• Experience with statistical software (such as Python, R and/or MATLAB) and database languages (SQL).
  
• Experience delivering insights from Artificial Intelligence (AI)/Machine Learning (ML) and statistical solutions to clients, including problem scoping/definition, modeling and interpretation.
  
• Experience with data extraction and modeling tools (SQL, Python).
  
• Understanding of AI/ML and statistical methods typically used in marketing analytics.
  

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.

In this role, you will help measure and optimize marketing Return on Investment (ROI) for Google’s largest clients. You will build bespoke models that address client’s key business questions. You willl have multi-disciplined professionals, PhDs, statisticians, economists, engineers, and former consultants in the team with deep experience in Data Science, Machine Learning (ML) and Marketing Analytics. You will learn the innovative technologies that drive Google products and bring those innovations to life in the context of specific client engagements.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.

Responsibilities

• Lead data science aspects of client engagements in the area of marketing effectiveness and marketing portfolio management with deep knowledge of Machine Learning (ML) and statistics.
  
• Collaborate with customers to solve their problems and identify the best statistical techniques, and develop 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.
  
• Work with customers and internal teams to translate data and model results into tactical and strategic insights that are actionable for decision-making. Co-present to and work with clients to integrate recommendations into business processes.
  
• Collaborate with Product/ Engineering teams to increase and optimize capabilities of our Applied Digital Skills team, employing methods which create opportunities for scale, proactively helping to drive innovation.