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  • ID
    #11990778
  • Job type
    Permanent
  • Salary
    USD TBD TBD
  • Source
    Uber Corporate
  • Date
    2021-04-07
  • Deadline
    2021-06-06
 
Permanent

About the team:

The Risk Feature Engineering platform works on software and services to provide easy to onboard, scalable feature engineering solutions for batch, streaming and real time features for ML rules and models for RIsk & Safety teams. You will be part of a team of strong software engineers executing in a fast paced environment. For more information of example of work tackled by Risk as a whole, see these blog posts:

https://eng.uber.com/uber- eats-risk-team/

https://eng.uber.com/ mastermind/

https://eng.uber.com/advanced- technologies-detecting- preventing-fraud-uber/

About the Role:

Team's mission:
  • Protect all Uber customers across all product lines (Rides, Eats, Freight, Uber for Business, Uber Everything) globally from fraudsters
  • Drive down global payment fraud
  • Build rich user experiences that not only block bad users but also make the in-app experience fluid and rich for our good users.
What you will do:
  • Detect fraudulent behavior through Machine Learning by leveraging account, location and device signals
  • Designing and developing backend systems infrastructure leveraging Cassandra, Hadoop/Hive, Flink and other Uber internal infrastructure platforms.
  • Work closely with a fun and highly collaborative cross-functional team of Engineers, Product Managers, Data Scientists, Data Analysts and Risk Analysts

Basic Qualifications:

  • At least two (2) years of software engineering experience
  • Bachelor of Science (BS) in Computer Science, Physics, or Mathematics, or related field
  • Experience coding with C, Java, Python, or Go
  • Experience developing and shipping code for production systems
  • Experience in systems software engineering. Sound understanding of computer architecture and CS fundamentals.
  • Proficient in one of the following programming languages: Java or Golang or other equivalent

Preferred Qualifications:
  • Detailed problem-solving approach and knowledge of algorithms, data structures, and complexity analysis.
  • Understanding of distributed system and architecture

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