Senior Data Scientist, Intelligent Automation and Recommendation

Minimum qualifications:

• Master's degree in Statistics, Data Science, Mathematics, Physics, Economics, Operations Research, Engineering, or a related quantitative field.
• 5 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 3 years of work experience with a PhD degree.
• Experience with statistical data analysis, statistical modelling and experimental design.

Preferred qualifications:

• 8 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or 6 years of work experience with a PhD degree.
• 4 years of experience as a data scientist, and experience applying advanced analytics to planning and infrastructure problems.
• Experience with Large Scale Computing and Predictive Modeling in a business context.
• Experience with Mathematical or Combinatorial Optimization.
• Experience designing and building machine learning models.
• Excellent problem-solving and project management skills.

Your role is to co-lead the transition of the resource management of hyperscale containerized platforms from ad-hoc heuristics to optimization, reinforcement learning and beyond. Working with product designers and tech leads, you will provide quantitative perspective and expertise in ML methods and statistical tools both to optimize the products and to evaluate their impact. You will have a unique opportunity to impact the key infrastructure layer for large-scale AI deployments spanning tens of thousands of machines and diverse products, including VertexAI and external customers.

• Collaborate with stakeholders in cross-projects and team settings to identify and clarify business or product questions to answer. Provide feedback to translate and refine business questions into analysis, evaluation metrics, or mathematical models.
• Gather information, business goals, priorities, and organizational context around the questions to answer, as well as the existing and upcoming data infrastructure.
• Own the process of gathering, extracting, and compiling data across sources via relevant tools (e.g., SQL, R, Python). Format, re-structure, or validate data to ensure quality, and review the dataset to ensure it is ready for analysis.
• Design and evaluate models to mathematically express and solve problems with limited precedent. Use statistical approaches to quantify uncertainty in data and in results.
• Use mathematical optimization/operational research methods to improve products' efficiency and customer experience.