Hoppa till huvudinnehållet

Berätta vilken plats du befinner dig på

Ange namnet på närmaste stad så att vi kan visa korrekt information för ditt område

Välj språk

2019 PhD Data Scientist Internship - Risk

Data Science, University i 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’re looking for PhD intern candidates to contribute to Risk data science in Summer 2019 (3 months) in our San Francisco and Palo Alto locations.  We seek candidates with strong background in statistics, machine learning, computer science, and operations research. You will be embedded in a cross-functional team such as Payment Risk, and work closely with the engineers, risk analysts, and product managers under the supervision of a data scientist in that team. You’ll be focused on modeling and algorithm development on a project defined by you and your mentor.  

What You'll Do


  • Work with your mentor closely to scope a project, define the problem, and develop and prototype the solution
  • Work alongside engineers, risk analysts and product managers to understand the business use cases and production feasibility of your work, and collaborate on the experimentation and productionisation of your work
  • Develop creative solutions to product problems using advanced mathematical algorithms such as machine learning and optimization
  • Communicate with senior management and cross-functional teams


Sample Projects


  • Develop machine learning models to detect payment fraud and marketplace abuse on Uber’s platform
  • Develop innovative features that leverage new sensor data and improve model performance
  • Develop NLP and computer vision models for user experience improvement
  • Develop graph based features and machine learning models to identify bad actors


What You’ll Need


  • Ph.D. student (anticipated graduation in 2020) majoring in Computer Science, Statistics, Operations Research, or any other quantitative disciplines
  • Experience in modeling and algorithm development
  • Coding proficiency and the ability to develop statistical analysis and prototype algorithms in Python or R.  Proficiency in Java is a plus
  • Ability to communicate effectively with both technical and business stakeholders


About the Team


The Risk Data Science team develops machine learning models to support multiple Uber business lines (Rides, Eats, U4B, etc). We are responsible for identifying bad actors while providing magical experiences to good riders and drivers. Our team works together with product managers, engineers, and risk analysts.

As a member of our team, you'll protect Uber's riders and drivers by bringing state of the art technology to bear on the world's richest dataset about how people move. You will experiment with a range of machine learning techniques including supervised, unsupervised and active learning approaches (e.g., tree-based models, CNN, LSTM, and DBSCAN), and tackle a variety of interesting and challenging problems including computer vision, NLP, knowledge graph, identity and reputation scores, mobile sensor and GPS feature development.


You can learn more about us through this blog post:

Se sekretessmeddelande för kandidat

På Uber accepterar vi inte bara olikheter, vi uppskattar, stöder och når framgångar genom dem. Detta gynnar våra medarbetare, produkter och medlemmar. Uber är stolt över att vara en arbetsplats där alla har samma möjligheter och en arbetsgivare som tror på skapande genom uppmuntran. Vi står för lika arbetsmöjligheter oavsett ras, hudfärg, börd, religion, könsidentitet, nationellt ursprung, sexuell läggning, ålder, medborgarskap, civilstånd, funktionshinder, könsidentitet eller veteranstatus.