Business and Marketing Data Scientist, Applied Machine Learning

Key Skills

Deep Learning, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, SQL, TensorFlow

Job Description

Minimum qualifications:

• Master's degree in Computer Science, Mathematics, Applied Statistics, Machine Learning, or equivalent practical experience.
• 3 years of experience in Deep Learning, Natural Language Processing (NLP), or Natural Language Understanding (NLU) and frameworks.
• 3 years of experience using SQL and programming in Python, TensorFlow or Pytorch.

Preferred qualifications:

• PhD in a quantitative discipline such as Computer Science or Engineering.
• Experience in driving a project from an experimental idea to a proof-of-concept to a launched product feature.
• Experience in cross-functional collaboration, with engineering teams and product teams.
• Publications and experience working with cutting-edge technologies.
• Experience with data ontologies with knowledge in graphs.

About the job

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 retention.

You will also be responsible for the regular and ad-hoc delivery of business growth incrementality of programs, as well as the design and statistical analysis of pilot results. You will partner with various teams to develop statistical models, customer-level recommendations, and automated solutions, consolidating existing Google technologies and building new ones. You'll also 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.

When our millions of advertisers and publishers are happy, so are we! Our Google Customer Solutions (GCS) team of entrepreneurial, enthusiastic and client-focused members are the human face of Google, helping entrepreneurs both individually and broadly build their online presence and grow their businesses. We are dedicated to growing the unique needs of advertising companies. Our teams of strategists, analysts, advisers and support specialists collaborate closely to spot and analyze customer needs and trends. In collaboration, we create and implement business plans broadly for all types of businesses.

Additional Information:

The US base salary range for this full-time position is $146,000-$220,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Build efficient and scalable ML models that help small and midsize businesses around the world 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.