Business and Marketing Data Scientist, Applied Machine Learning

In this role, you will 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. You will build recommendation engines and impact measurement tools for Google Customer Solution Sales and Marketing to increase impact and operational effectiveness across the customer journey. You will also 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.

Google Customer Solutions (GCS) sales teams are trusted advisors and competitive sellers who maintain a relentless focus on customer success by bringing the best Google has to offer to small- and medium-sized businesses (SMBs), which are the backbone of our communities. As a member of our team, you’ll have the opportunity to work with company owners and make a real difference in their businesses by helping them grow. Together, we help shape the future of innovation for customers, partners, and sellers...and we 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.
  

Minimum qualifications:

• 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.