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Metro location, general Data Scientist title, and broad MLOps+cloud skill requirements increase applicant competition.
Role requires NBFC credit-risk, bureau-data, and decisioning experience, limiting transferability across industries.
Many mandatory technical skills (PySpark, Docker, Kubernetes, AWS) and domain-specific credit risk requirements.
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Develop, deploy, and maintain production-grade credit risk and decisioning models using various data sources including bureau and transactional data.
Build scalable, robust feature engineering pipelines with Python, PySpark, and SQL for large-scale structured and semi-structured datasets.
Manage model deployment and integration with decision engines ensuring low-latency inference using containerization (Docker, Kubernetes) and implement MLOps practices including testing, monitoring, and version control.
Experience working in NBFC/banking environment focused on credit risk or decisioning models.
Proficiency in Python, PySpark, SQL for data pipeline development and feature engineering.
Experience with containerization tools (Docker, Kubernetes) and model deployment in production.
Work Experience Required: Not explicitly mentioned in the JD.
Strong operational focus on end-to-end machine learning pipelines from data ingestion to production deployment with measurable business impact.
Technical experience integrating models into decision engines with emphasis on scalability, performance, and monitoring.
Comfortable working in cloud-based (AWS or equivalent) distributed systems with cross-functional teams (risk, product, data engineering).