Uber Staff Applied Scientist - Capacity & Efficiency Engineering 

United States, West Virginia 

953777974

• Develop forecasting models and iterate on model refinement to minimize forecasting errors by developing a deeper understanding of how critical Uber infrastructure components scale
• Derive insights from data to identify opportunities for efficiency and resource consumption reduction across the Uber infrastructure
• Use statistical modeling techniques to develop northstar metrics and KPIs to help provide a more rigorous data-driven approach to manage Uber infrastructure
• Develop simulation models for the Uber infrastructure to improve our understanding of the various cost drivers and how to best control them, while managing risk, reliability and availability
• Conduct ad-hoc analysis, reporting, and build visualizations to communicate findings to Engineering Leadership
• Present findings to senior leadership to drive business decisions

• 8+ years of working experience as an applied scientist in the tech industry
• Ph.D. or M.S. degree in Statistics, Economics, Mathematics, Machine Learning, Operations Research, or other quantitative fields, or equivalent industry experience
• Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics
• Advanced knowledge and experience in time-series forecasting, anomaly detection and building ML models in production
• Ability to use Python and Apache Spark to work efficiently at scale with large data sets
• Proficiency in libraries, languages, technologies and tools like R, SQL, pandas, numpy, pyspark

• Expertise with BI tools such as Tableau
• Experience in algorithm development and prototyping
• Exposure to the infrastructure domain, particularly capacity engineering
• Exposure to financial analysis
• Exposure to large scale simulations

*Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .