Job Description:
• Develop and deploy machine learning models to prevent and detect fraud and abuse, leveraging structured and unstructured data sources.
• Own the end-to-end ML lifecycle, including data preprocessing, feature engineering, model training, evaluation, validation, and deployment.
• Monitor and maintain models in production to ensure performance and reliability over time.
• Collaborate with product and engineering teams to integrate machine learning models into production applications.
• Foster a culture of learning, experimentation, and collaboration within and across partner teams.
Requirements:
• 3+ years of experience building and deploying production machine learning models.
• Previous experience building fraud detection or risk assessment tools is a strong plus.
• Solid understanding of fundamental machine learning and computer science concepts, software design best practices.
• Expertise with Python, including common ML/AI libraries such as Scikit-learn, Pytorch, or Tensorflow.
• Expertise with SQL; experience with dbt or graph databases is a plus.
• Familiarity with large language models (LLMs) and their applications in risk and fraud detection.
• Experience with AWS, cloud computing, and/or Typescript is a plus.
• Excellent communication and stakeholder management skills, with a track record of working cross-functionally to drive business impact.
• Attention to detail, intellectual curiosity, and a deep understanding of user behavior and fraud patterns.
• Empathy and humility.
Benefits:
• Competitive salary based on experience, with full medical and dental & vision benefits.
• Stock in an early-stage startup growing quickly.
• Generous, flexible paid time off policy.
• 401(k) with Financial Guidance from Morgan Stanley.