Data Scientist - Anti-Fraud

Data Scientist – Anti-Fraud

Familiarity with fraud domains and banking processes, including account takeover, ACH fraud, debit/credit card fraud, first-party fraud, and synthetic identity fraud.
Demonstrated expertise in building and deploying fraud models using large datasets, with a strong track record of success in fraud detection and prevention.
Graduate degree in a quantitative field such as mathematics, economics, statistics, engineering or natural sciences (or equivalent research experience)
Solid understanding of unsupervised learning, statistical analysis and machine learning algorithms for imbalanced datasets.
Excellent programming skills, including familiarity with either Python (numpy, scipy, pandas), sql, tensorflow, spark
Experience with experimentation and communicating data driven insights
2 + years professional experience as a Data Scientist / Machine Learning Engineer
Passion for working and learning in a fast-growing company

Combining knowledge of several research domains to improve our understanding of different risks to Robinhood and help power decisions
Designing new machine learning systems to power the fraud prevention and risk reduction efforts at Robinhood especially in product areas
Build production grade models on large-scale datasets to measure effectiveness across products by leveraging statistical modeling, machine learning and data mining techniques.
Collaborate with the rest of the data team and partner marketing, product, content, design teams to build data solutions and products to drive user and revenue growth.
Work with cross-functional teams to implement insights and analytical solutions to empower data-driven decision making.
Problem solving skills and a can-do attitude to dive deep into data to solve business problems