2019 PhD Data Scientist Internship - Forecasting and Anomaly Detection Platform
Data Science, University in San Francisco, CA
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.
About the Role
We are looking for a PhD intern candidates to join the Forecasting and Anomaly Detection Platform team for the Summer of 2019 (3 months). During that time the intern will develop new methods that address our complex problem space working closing with a team of experienced Data Scientists and domain experts.
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
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 include Hierarchical Forecasting, Deep Learning, Bayesian Forecasting, Probabilistic Programming, as well as developing novel statistical models. Our work helps to create technology that ensures the Uber experience is always excellent.
Be sure to check out the Uber Engineering Blog to learn more about the team.
At Uber we don’t just accept difference—we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.