About the Role
We're looking for a Data Scientist to join the Risk Data Science team to apply a data-driven approach to identify, understand, scope, and remediate emerging fraud trends on Uber platform. In this role, you'll be part of and work closely with a cross-functional team consisting of engineers, product managers, operations, and other data scientists. This role will be responsible for not only measuring but also directly implementing solutions that help improve key performance metrics such as fraud losses, false positives, and operational efficiency. You will require a mix of business and technical acumen and also cross-functional skills to communicate with various internal and external stakeholders.
What the Candidate Will Need / Bonus Points
- What the Candidate Will Do -
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Perform statistical analysis to understand Risk / Fraud behaviors, contribute to fraud detection features and models;
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Build and maintain fraud rules in response to evolving fraud behaviors;
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Extract insights from the large volumes of data and come up with new strategies to mitigate/stop fraudulent activities;
- Build deep understanding of the Risk data, reporting, and key metrics;
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Conduct experimentations to test and optimize the effectiveness of risk mitigation solutions and products.
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Participate in project definition and idea generation, work on collaborative projects with stakeholders across the globe such as product, engineering, comm ops and data science with a focus on the Risk/Fraud mitigation;
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Effectively communicate and present findings to the management team to strengthen business decisions.
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With guidance from manager, define and develop area of expertise;
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Attend regular training courses, functional business review meetings, and all-hands;
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Stay highly engaged and always hustle as Uber Risk is a very fast-paced environment
- Basic Qualifications -
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Minimum 1 years of experience in a data-focused role such as product analytics, business analytics, business operations, or data science
- Bachelors or Masters in Mathematics, Statistics, Computer Science, Operational Research, Economics or other quantitative field.
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Proven track record of applying analytical/statistical methods to solve real-world problems using big data.
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Creative problem-solving, critical thinking skills, and get things-done attitude.
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Advanced SQL expertise.
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Experience using Python.
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1yrs+ experience in improving products, understanding the user journey, and extracting insights from data.
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Understanding of experimental design and using statistical methods in exploratory analysis.
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Experience with dashboarding/data visualization (i.e. Google Data Studio, Tableau, Mixpanel, Looker or similar).
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Comfort with ambiguity and the ability to work in a self-guided manner
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Superb communication and stakeholder management skills
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Proven aptitude toward Data Storytelling and Root Cause Analysis using data
- Preferred Qualifications -
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Risk/Fraud/Payments experiences a plus
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Experience in experimentation, A/B testing and statistical modeling would be a plus
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Experience in Python/R
