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 Responsibilities1. 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.