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 lear... more details
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.
Additionally, you will 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'll partner with various teams to develop statistical models, customer-level recommendations and automated solutions, consolidating existing Google technologies and building new ones. You will 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.
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.
The US base salary range for this full-time position is $150,000-$223,000 + 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.
- 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.
- 4 years of experience in Deep Learning, Natural Language Processing (NLP), or Natural Language Understanding (NLU) and frameworks.
- 4 years of experience using SQL and programming in Python, TensorFlow, or Pytorch.
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.
