About the Role
The Capacity & Efficiency Engineering (CEE) organization is looking for an Applied Scientist to join us in Amsterdam. In this role you will design and build forecasting models to better understand all growth drivers that require Uber's infrastructure to scale to meet product and business needs. Furthermore, you will contribute towards building a broad understanding of Uber's consumption of resources across its entire tech stack, identify cost-savings opportunities and develop simulation models to help Uber save significant resources in terms of money, time and effort.
An ideal candidate will be working closely with a highly cross-functional team, including product management, engineering, tech strategy, and leadership to help drive down the cost of Uber's infrastructure. A successful candidate will need to demonstrate strong technical skills, including coding, advanced statistics, experimentation, causal inference techniques, and machine learning, while also being able to present results to a senior leadership audience.
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What you will do
- 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
Basic Qualifications
- 3+ 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
Preferred Qualifications
- Experience in algorithm development and prototyping.
- Exposure to the infrastructure domain, particularly capacity engineering
- Exposure to financial analysis
- Exposure to large scale simulations
We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let's move the world forward, together.
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.
*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 accommodations@uber.com.