Business and Marketing Data Scientist, Applied Machine Learning

In this role, you will partner with Engineering, Product, and Finance teams across Google to develop and deliver machine learning and predictive analytics solutions at scale for Sales and Marketing stakeholders. You will build recommendation engines and impact-measurement tools for Google Customer Solutions Sales and Marketing to improve impact and operational effectiveness throughout the customer journey.

You will build, test, and scale statistical and machine learning models that measure and amplify advertiser impact from acquisition through growth and continuation. You will deliver regular and ad-hoc analyses of program growth incrementality, design and analyze pilots, and develop customer-level recommendations and automated solutions. The team uses Google's data and machine learning to generate insights at scale that guide long-term strategy and near-term sales and marketing operations.

Google Customer Solutions teams help small- and medium-sized businesses grow by bringing them the best Google has to offer. This listing is based in New York City. Compensation is $138,000–$198,000, plus a 15% bonus target, equity, and benefits.

Minimum Qualifications: Master's degree in Computer Science, Mathematics, Applied Statistics, Machine Learning, or equivalent practical experience. Three years of experience using analytics to solve product or business problems, coding in Python, R, or SQL, querying databases, or performing statistical analysis; alternatively, a relevant PhD.

Preferred Qualifications: PhD in Computer Science, Engineering, or a related field. Experience driving a project from an experimental idea and proof of concept to a launched product feature. Experience collaborating cross-functionally with engineering and product teams. Publication experience involving relevant technologies. Experience with data ontologies and knowledge graphs.