We’re changing the way people think about transportation. Not that long ago we were just an app to request premium black cars in a few metropolitan areas. Now we’re a part of the logistical fabric of more than 600 cities around the world. Whether it’s a ride, a sandwich, or a package, we use technology to give people what they want, when they want it.
For the people who drive with Uber, our app represents a flexible new way to earn money. For cities, we help strengthen local economies, improve access to transportation, and make streets safer.
And that’s just what we’re doing today. We’re thinking about the future, too. With teams working on new modalities, self-driving cars and even urban air transportation, we’re in for the long haul. We’re reimagining how people and things move from one place to the next.And that’s just what we’re doing today. We’re thinking about the future, too. With teams working on new modalities, self-driving cars and even urban air transportation, we’re in for the long haul. We’re reimagining how people and things move from one place to the next.
About the role
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
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
- A graduate degree or equivalent in a quantitative domain (e.g. statistics, mathematics, computer science)
- 3+ years of delivering, scaling, and owning highly successful and innovative data science products with your fingerprints all over them - you're extremely proud of what you've accomplished
- 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.