Brazil Job Openings
Uber
Data Scientist - Risk & Fraud
São Paulo
FULL TIME
October 18, 2024
Risk DS team is spread across US, Amsterdam, Brazil, and India and is primarily focused on managing global fraud losses across Lo Bs with real-time strategies. We balance loss rate with action rate to both make attackers pursue other avenues and allow good affected users to have proceed.
The key questions we ask ourselves daily: "How exactly did this attacker bypass our layered defence?" and "What is the best sustainable way to prevent this from scaling?"
We run risk product rollouts (e.g. a new user challenge or a new kind of actioning) and implement models built by MLEng.
What the Candidate Will Need / Bonus Points- What the Candidate Will Do -
- Perform statistical analysis to understand fraud behaviours and contribute to detection models/features
- Build and maintain fraud rules to address evolving fraudulent activities
- Extract insights from large datasets to develop fraud mitigation strategies
- Build deep understanding of risk data, reporting, and key metrics
- Conduct to and optimize risk mitigation solutions
- Collaborate with global cross-functional teams on fraud prevention projects
- Effectively communicate findings to drive business decisions
- With guidance from manager, define and develop an area of expertise
- Immediate availability to work from São Paulo;
- English proficiency (B2+);
- Experience in a data-focused role, such as data science, fraud analytics or risk management;
- Exceptional proficiency in and statistical analysis languages (, R, or similar);
- Proven track record of leveraging advanced analytical techniques and statistical methods to solve complex, real-world problems;
- Experience in testing, and statistical modeling;
- Expertise in defining, measuring, and communicating performance metrics that drive business impact;
- Excellent communication skills and the ability to articulate technical concepts to diverse stakeholders.
- Advanced degree in a quantitative field such as Statistics, Mathematics, Operations Research, Economics, or a related discipline
- Data engineering/pipeline creation experience
- Prior background in risk, fraud, or payments
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