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

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

Quant-data.ioAnand, IN
9 hours ago
Job description

🚀 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.
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