Senior Machine Learning Engineer, Fraud

• Investigate fraud patterns and model errors to identify new signals, improve detection, and expand coverage across customers and use cases
• Develop training datasets and predictive features, addressing incomplete labels, class imbalance, data leakage, and changing fraud behavior
• Design, train, and tune models using traditional and modern ML methods, including gradient-boosted trees and neural networks
• Evaluate newer model architectures against existing approaches
• Design experiments to compare feature and model performance across time periods and customer segments using detection and false-positive metrics
• Build data and training pipelines supporting reproducible experiments and efficient iteration
• Deploy models with Engineering and ML Infrastructure partners, balancing detection quality, latency, cost, and reliability
• Independently lead ML projects from initial experiments through model release, aligning priorities and evaluation metrics with Data Science and Product
• Take models through production and evaluate impact using real-world customer outcomes
• Develop and scale reliable ML systems in production
• Explore LLMs and Generative AI for fraud detection, prevention, and investigation

• 7+ years of professional experience in machine learning, applied science, or software engineering for ML, including hands-on model development and deployment
• Hands-on experience designing, training, tuning, and deploying models, and measuring improvements in production performance or business metrics
• Strong ML and statistical fundamentals, including feature engineering, experiment design, model evaluation, and diagnosing why a model under performs
• Strong understanding of traditional and modern ML methods, including gradient-boosted trees and neural networks
• Experience constructing training datasets and addressing label quality, data leakage, class imbalance, and generalization across time periods or populations
• Strong Python skills
• SQL proficiency for working with training and evaluation data
• Hands‑on experience with ML frameworks such as PyTorch, scikit-learn, XGBoost, or equivalents
• Experience independently leading ML projects from an open-ended problem through deployment, coordinating requirements and model releases with Data Science, Product, and Engineering
• Fraud or risk modeling experience is strongly preferred
• Experience developing models that generalize across customers with different data and behavior patterns
• Experience using graph‑based systems to extract predictive signals, uncover fraud patterns, and improve fraud model performance
• Experience applying learned representations, transformers, or foundation models to improve a production ML use case

Demonstrates extensive experience in machine learning model development, deployment, and evaluation, with a strong focus on fraud detection and prevention. Proficient in designing experiments, feature engineering, and utilizing modern ML methods to enhance model performance across diverse customer behaviors.

• Machine Learning Model Development
• Fraud Detection and Prevention
• Python Programming
• SQL Proficiency
• ML Frameworks (PyTorch, scikit-learn, XGBoost)

• Model Training
• Model Tuning
• Feature Engineering
• Experiment Design
• Model Evaluation
• Data Leakage Management
• Class Imbalance Handling
• Graph-Based Systems
• Transformers
• Foundation Models

• Project Leadership
• Collaboration
• Problem Solving

• Fraud Modeling
• Risk Modeling
• Predictive Features
• Customer Segmentation
• Generative AI

• ML Infrastructure
• Data Pipelines
• Reproducible Experiments