Business and Marketing Data Scientist, Applied Machine Learning

About the role

Work in close partnership with several Engineering, Product, and Finance teams across Google to develop and deliver machine learning and predictive analytics solutions at scale to our Sales and Marketing stakeholders.

Build recommendation engines and impact measurement tools for Google Customer Solution Sales and Marketing to increase impact and operational effectiveness across the customer journey.

Build, test, and scale statistical and machine learning models that measure and amplify impact across the entire advertiser journey from acquisition to growth and continuation.

Deliver business growth incrementality of programs, and design and statistical analysis of pilot results.

Partner with various teams to develop statistical models, customer-level recommendations and automated solutions, consolidating existing Google technologies and building new ones.

Work with others on the team to harness the power of Google’s data with machine learning to provide insights at scale that drive both long-term strategy and near-term operations for sales and marketing.

Help shape the future of innovation for customers, partners, and sellers, and have fun doing it.

Responsibilities

• Build efficient and scalable Machine Learning (ML) models that help small and mid-size businesses to grow their business, leveraging the power of Google solutions.
• Solve real-world problems with the latest research in deep learning, natural language processing, and understanding.
• Work with product teams to understand their objectives, product requirements, constraints, and key metrics.
• Propose, build, evaluate, and debug machine learning models and algorithms.
• Integrate pipelines, models, and predictions into production serving systems.

Requirements

• Master's degree in Computer Science, Mathematics, Applied Statistics, Machine Learning, 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

• PhD in Computer Science or Engineering, or a related field.
• Experience in driving a project from an experimental idea, proof-of-concept, and a launched product feature.
• Experience in cross-functional collaboration, with engineering and product teams.
• Experience in publications working with technologies.
• Experience with data ontologies with knowledge in graphs.

Benefits

Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $138000 - $198000 (USD) + 15% bonus target + equity + benefits