Data Scientist - Risk & Identity
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 Risk Data Scientist to join the team. Working as a data scientist in the risk domain is like playing an adversarial game with fraudsters: you will frequently work to identify new fraud patterns and provide scientific solutions to address emerging risk problems at scale. 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 and analysts. The ideal candidate will be passionate about this type of problem to balance the historical patterns and new ones, and effectively collaborate with various internal and external partners.
What You'll Do
- Establish holistic research based on extensive data to comprehensively understand the problem at hand, such as data collection, cleaning, and tabulating; collaborate with others to formulate an executable plan
- Build machine learning models, optimization solutions and statistical inference techniques to address the fraud and financial abuse across lines of business
- Communicate results on a regular basis to partners around the world, including executive leadership
- Manage individual project priorities, deadlines and deliverables in a fast paced, iterative development environment
- With guidance from your manager, define and develop an area of expertise/specialization
- Attend regular training courses, functional business review meetings, and all-hands
- Stay highly engaged and always hustle as Uber Risk is a very fast-paced environment
- Ph.D., M.S. or Bachelors degree in Statistics, Economics, Machine Learning, Operations Research, 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 as a Data Scientist or equivalent)
- Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, economics, and analytics
- Knowledge of experimental design and analysis
- Experience with exploratory data analysis, statistical analysis and testing, and model development
- Ability to use a language like Python or R to work efficiently at scale with large data sets
- Proficiency in tools like SQL, Hive, and Spark
- 2+ years of industry experience in data science or other relevant data-focused roles; Risk/Fraud/Payments experience a plus
- Hands on experience with Hive, Spark, Hadoop or other large-scale data frameworks is a plus
- Experience in productionizing algorithms
- Comfort with ambiguity and the ability to work in a self-guided manner
- Business driven mentality with insightful data interpretation, critical thinking about how to effectively combat fraud while maintaining the health of business growth
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.
Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, Veteran Status, or any other characteristic protected by law.
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