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

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.