What you'll do
- Push the envelope on what can be done in the realm of time series and anomaly detection, by actively researching and developing the next generation algorithms. Implement these methodologies in a rapidly growing platform designed for broad adoption and ease of use.
- Partner with experienced scientists and engineers in building first-class products
What you'll need
- Ph.D. student graduating in 2019 majoring in a quantitative domain (e.g. statistics, mathematics, computer science)
- Deep knowledge of statistical principles and Machine Learning methods. Previous experience in time series forecasting and anomaly detection is a plus
- Demonstrable proficiency in writing production level code (Python, Go, R preferred) and understanding programming concepts, combined with the enthusiasm and passion to build.
About the Team
Time Series analysis is central to Uber in many ways:
- accurate forecasts are essential for informed decision making
- prompt detection of anomalies insures reliability
- short-term automated forecasting powers optimization
To accomplish these goals, the Forecasting and Anomaly Detection Platform develops state-of-the art Machine Learning techniques and deploys them as scalable tools. Active areas of research for us are Hierarchical Forecasting, Deep Learning, Bayesian Forecasting, Probabilistic Programming, as well as developing novel statistical models.
Our work helps creating technology that insures the Uber experience is always excellent.
A sample of our team's work can be found in
At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 600 cities around the world.
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.