Senior Machine Learning / Data Engineering / Data Science Engineer Credit Risk

Role Overview: As a Senior Machine Learning / Data Engineering / Data Science Engineer specializing in Credit Risk, you will be responsible for building Next-Generation AI-Powered Credit Decisioning Systems on AWS. You should have at least 5 years of experience and a deep understanding of credit risk modeling, lending workflows, and end-to-end credit decisioning systems. Your role will involve designing and deploying production-grade models, data pipelines, APIs, and governance frameworks to support modern lending products. Key Responsibilities: - Develop and validate credit scoring, PD/LGD/EAD, and behavioral/collections models. - Build rule-based + ML hybrid underwriting engines and challenger models. - Design and implement feature stores, scorecards, segmentation logic, and reason-code/XAI frameworks. - Build large-scale ETL/ELT pipelines using AWS and open-source stacks (Airflow, Spark, Trino, EKS, S3, EC2). - Implement robust data quality, lineage tracking, anomaly detection, and incremental loading. - Optimize compute and storage for performance, reliability, and cost (including Graviton). - Deploy models using MLflow, Flask, or FastAPI. - Implement model monitoring, drift detection, CI/CD, and automated retraining workflows. - Ensure compliance with Basel III, SR 11-7, GDPR, PDPA using explainability and governance tools. - Build dashboards for model performance, data quality, and underwriting analytics. - Build and deploy APIs using API Gateway, Lambda, ECS/Fargate, or EKS. - Integrate ML scoring pipelines with LOS/LMS, credit bureaus, and partner systems. - Conduct demos, PoCs, and technical workshops with clients. - Translate business problems into credit product workflows, decision rules, and risk logic. Qualifications Required: - 5+ years in Machine Learning, Data Engineering, or Data Science. - Hands-on experience building credit risk, fraud, or behavioral ML models. - Strong expertise in Python, PySpark, SQL, and ML frameworks (scikit-learn, XGBoost, TensorFlow, PyTorch). - Experience with Spark, Hadoop, Kafka, Trino/Presto. - Strong understanding of credit underwriting workflows, lending KPIs, and risk decisioning. - Experience building and deploying ML scoring APIs. - Familiarity with MLOps best practices and production ML systems. - Strong grasp of data governance, regulatory compliance, and model documentation. Additional Company Details: N/A,