Minimum qualifications:
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
- 4 years of experience in a Data Science role using statistics and machine learning to solve product or business problems.
- 4 years of experience using SQL and programming in Python, TensorFlow, or Pytorch.
- Experience in cross functional collaboration, with program management, go-to-market, engineering, or product teams.
- PhD in a quantitative discipline such as Computer Science, Engineering, Statistics, Economics, Operations Research, or equivalent practical experience.
- 6 years of industry experience in a Data Science role (4 years with PhD) using statistics and machine learning to solve product or business problems
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About the job
The Revenue Acceleration team (RevX) is focused on boosting the growth of Google Cloud with scalable and data driven strategies. The team is highly cross-functional, combining Strategy and Operations and Data Science. The team serves many functions, including designing business workflows, improving business programs, and developing tools and incentive systems to accelerate business performance. As a Data Scientist on the Revenue Acceleration team, you will be solving difficult and non-routine problems by applying Analytics, Statistics, ML, and GenAI to achieve results. You will process, analyze, and interpret large and complex data sets, with emphasis in delivering data-driven insights and solutions. You will also collaborate closely with Engineers, Program Managers, and other stakeholders to implement ML solutions, measure the effectiveness of programs, and drive customer growth and success.
Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Process large amounts of data from multiple sources and extract relevant insights.
- Partner with non-technical stakeholders to understand their analytical needs, help frame the problem by asking the right questions, and prioritize requirements. Effectively communicate results of technical work to non technical audiences.
- Produce actionable data-driven insights, perform statistical analyses, build Machine Learning solutions , and design experiments to measure the effectiveness of models, tools, and programs.
- Research new ways of modeling data proactively to unlock actionable insights or improve business processes.