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2019 PhD Data Scientist Internship - Forecasting and Anomaly Detection Platform

San Francisco, CA の Data Science, University

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

 

Sample Projects

 

What You'll Need

 

  • PhD candidacy (anticipated graduation in 2020) majoring in Data Science, Statistics, Machine Learning, Physics, Computer Science or other quantitative disciplines
  • Strong knowledge of statistical principles and machine learning methods.
  • A curious mind
  • Proficiency in writing good quality code

 

About the Team

 

Time Series analysis is central to Uber for a variety of reasons:

  • accurate forecasts are essential for informed decision making
  • prompt detection of anomalies ensures 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 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.


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