Role: Data Scientist Credit Scoring & Analytics Implementation

Hands-on experience : Machine Learning model implementation and deployment, SQL, Python.

Key Responsibilities

- Lead the design, development and validation of credit risk scorecard models using ML, AI,and other statistical techniques, using Financial and Alternate Data.
- Perform advanced exploratory data analysis (EDA), feature engineering, and data preparation on large, complex datasets
- Translate business and risk requirements into analytical solutions and support their integration into production systems. (e.g., AUC, KS, Gini)
- Own end-to-end model lifecycle: development, validation, deployment and ongoing monitoring
- Partner with engineering teams to integrate models into production systems (APIs, batch scoring,real-time decisioning) and collaborate with application teams to ensure robust and scalable implementation of scoring logic
- Develop monitoring frameworks, dashboards, and reports to track model performance, drift and portfolio health
- Produce high-quality technical documentation, validation reports, model reports and stakeholder presentations
- Provide technical guidance, best practices and task allocation to team members and support knowledge transfer across teams.





Required Technical Skills

Must Have

- 810 years of experience in ML models implementation & credit risk technology solutions
- Deep understanding of statistical modelling techniques (logistic regression, WOE/IV, binning, model validation) and machine learning methods
- Strong proficiency in Python (preferred) or similar analytical tools (e.g., SAS, STATA)
- Strong understanding of .NET / C# based applications and system integration
- Advanced SQL skills and experience working with large-scale relational databases (e.g., Oracle,SQL Server, Postgres and MongoDB)
- Experience managing analytics or technology delivery projects
- Robust communication skills

Good to Have

- Basic understanding of credit risk modelling / scorecard concepts
- Familiarity with BI and visualization tools such as Power BI
- Knowledge of regulatory frameworks in credit risk (e.g., IFRS 9, Basel III)
- Experience with cloud platforms (AWS, Azure, or GCP)

Impact

- Drive credit risk strategy through robust, production-grade models
- Improve portfolio performance and decision accuracy

Shape best practices in model development, deployment, and monitoring

📌 Senior Data Scientist (Chennai)
🏢 Dun & Bradstreet Technologies and data services
📍 Chennai