Uber is looking to solve the problem of safeguarding the integrity of the platform by identifying, analyzing, and mitigating emerging trends in fraud. The goal is to develop robust strategies that minimize losses while ensuring operational efficiency and a seamless user experience.
Requirements
- Minimum of 1 year of experience in a data-focused role, such as data science, analytics, management, or business intelligence
- Exceptional proficiency in and statistical analysis languages (SQL, R, or similar)
- Proven track record of leveraging advanced analytical techniques and statistical methods to solve complex, real-world problems
- Experience in hypothesis testing and statistical modeling
- Expertise in defining, measuring, and communicating performance metrics that drive business impact
- Data engineering/pipeline creation experience
- Familiarity with statistical optimization under unsupervised learning, boosted trees, neural networks, or natural language processing architectures.
Responsibilities
- Perform statistical analysis to understand behaviours and contribute to detection features
- Build and maintain rules to address evolving fraudulent activities
- Extract insights from large datasets to develop fraud mitigation strategies
- Build deep understanding of data, reporting, and key metrics
- Conduct to and optimize mitigation solutions
- Collaborate with global cross-functional teams on prevention projects using the full variety of statistical learning solutions
- Effectively communicate findings to drive business decisions
Other
- Collaborating with cross-functional teams of engineers, product managers, fellow decision scientists, and operations managers
- With guidance from manager, define and develop an area of expertise
- A natural problem-solver with a passion for critical thinking and a "get things done" mindset
- Good communication skills and the ability to articulate technical concepts to diverse stakeholders
- Comfortable with ambiguity and capable of thriving in a dynamic, self-directed environment
