Risk Data Science and Analytics teams provide insights and develop machine learning models and strategies to combat payment fraud and marketplace abuse, improve account security and integrity, and minimize credit risk for financial products.
About the Role
We're looking for a Decision Scientist to join the team to build data-driven strategies to address emerging fraud trends on the Uber platform. In this role, you'll work closely with a cross-functional team consisting of engineers, product managers, operations, and data scientists. This role will be responsible for key performance metrics such as fraud losses, false positives, and operational efficiency. You will require a mix of business and strong technical acumen as well as cross-functional collaboration skills to drive fraud strategy across various internal and external partners.
What You'll Do

• Use data to understand Risk / Fraud behaviors and build fraud detection features, rule strategy and models that mitigate/stop fraudulent activities;
• Build and maintain fraud features, rules, and models in response to evolving fraud behaviors;
• Develop a deep understanding of Risk / Fraud data, reporting, and key metrics;
• Participate in product definition and idea generation activities by working on collaborative projects with partners across the globe such as product, engineering, comm ops, and data science with a focus on Risk/Fraud mitigation;
• Communicate and present findings to Uber management teams to strengthen business Risk Decisions.
• Define and develop Uber Risk / Fraud areas of expertise;
• Attend regular training courses, functional business review meetings, and all-hands;

Basic Qualifications

• M.S. or Bachelors degree in Mathematics, Statistics, Computer Science, Economics, or other quantitative fields. (If M.S. degree, a minimum of 1+ years of industry experience required and if Bachelor's degree, a minimum of 2+ years of industry experience in a data-focused role such as product analytics, business analytics, business operations, or data science)
• Knowledge of Risk/Fraud/Payments experience
• Advanced SQL ability
• Experience with data analysis and visualization tools, such as Python, R, Tableau
• Advanced knowledge of experimentation - design (A/B Testing), and analysis
• Proficient at defining, applying, and presenting performance metrics
• Proven track record of applying analytical/statistical methods to tackle real-world problems using big data
• Hybrid role (relocation available)

Preferred Qualifications

• 3+ years of SQL experience (Hive, Spark, and other big data tools a plus)
• 3+ years of experience in decision science, data science, data analysis, or other quantitative fields
• 3+ years of experience related to Risk/Fraud/Payments
• 1+ years of experience in experimentation, A/B testing, and statistical modeling would be a plus
• Advanced degree in Mathematics, Statistics, Computer Science, Economics or other quantitative disciplines
• Experience in using statistical packages in Python/ RStudio
• Creative problem solving, critical thinking skills, and get things done attitude

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 a curiosity, passion and collaborative spirit, work with us, and let's move the world forward, together.
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
If you have a disability or special need that requires accommodation, please let us know by completing this form.