Data Scientist II - Fraud

Experience working on fraud or fraud-adjacent data sets. Master's degree or higher in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent professional experience. Proficiency in Python (preferred) or R, with hands-on experience in machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost. Demonstrated ability to analyze, clean, and model large-scale datasets using SQL and modern data tools (e.g., AWS, Databricks, Hadoop/Spark). Create dashboard in AWS Quicksight and Databricks Working knowledge of supervised and unsupervised learning, feature engineering, and model evaluation approaches. Experience translating business challenges into data science solutions and clearly communicating outcomes.

Design, develop, and implement machine learning models and statistical algorithms for fraud detection and identity verification. Analyze large datasets to uncover fraud patterns and opportunities for product enhancement. Understand feedback/outcome and fraud contribution data to improve models. Understand FCRA data and model design. Collaborate with product, engineering, and cross-functional teams to translate business requirements into data-driven solutions. Develop and code data processing pipelines, automated workflows, and tools. Provide analytical support to the fraud and risk data science team and communicate findings. Test and apply the latest machine learning algorithms, libraries, and techniques. Build, maintain, and monitor robust, scalable models deployed into production. Contribute to a collaborative, high-performance team environment.