Machine Learning Engineer II, ML Fraud

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Requirements:

• 2+ years of experience as a machine learning engineer or PhD in a relevant field.
• Proficiency in machine learning, with experience in gradient boosting, online learning, and deep learning.
• Domain knowledge in fraud risk is a plus.
• Strong programming skills in Python.
• Experience with large-scale distributed systems like Spark and Ray.
• Experience with machine learning frameworks such as scikit-learn, pandas, numpy, xgboost, and pytorch.
• Excellent communication skills and ability to drive cross-functional requirements.
• Ability to present technical concepts in audience-appropriate ways.
• Persistence, patience, and a strong sense of responsibility.

Responsibilities:

• Use proprietary and third-party data to develop machine learning models that predict likelihood of fraud.
• Partner with the ML platform team to build fraud-specific ML infrastructure.
• Research groundbreaking solutions and develop prototypes for fraud decisioning.
• Implement and scale data pipelines, new features, and algorithms essential to production models.
• Collaborate with engineering, fraud, and product teams to define new product requirements.
• Develop fraud models to maximize user conversion while minimizing losses.