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

🚀 Build Next-Generation AI-Powered Credit Decisioning Systems on AWS We’re seeking a highly experienced engineer (5+ years) with deep expertise in credit risk modelling , lending workflows , and end-to-end credit decisioning systems . You will design and deploy production-grade models, data pipelines, APIs, and governance frameworks that power modern lending products. 🔍 Key Responsibilities 1. Credit Risk Modelling & Decisioning Develop and validate credit scoring , PD / LGD / EAD , and behavioural / 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. 2. Data Engineering & Architecture (AWS) 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). 3. MLOps & Governance 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. 4. APIs & Integration Build and deploy APIs using API Gateway, Lambda, ECS / Fargate, or EKS. Integrate ML scoring pipelines with LOS / LMS , credit bureaus, and partner systems. 5. Product Solutioning & Pre-Sales (Good to Have) Conduct demos, PoCs, and technical workshops with clients. Translate business problems into credit product workflows , decision rules, and risk logic. 🎯 Required Skills & Experience 5+ years in Machine Learning, Data Engineering, or Data Science. Hands-on experience building credit risk, fraud, or behavioural 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. ⭐ Preferred Qualifications AWS experience with VPC, ECS / EKS, S3, IAM, Athena, Lambda. Background in banks, NBFCs, fintechs, or credit bureaus. Pre-sales or client-facing solutioning experience. Exposure to alternative data modelling. Degree in Computer Science, Data Science, Statistics, Engineering , or related fields.