Senior Data Scientist

Dun & Bradstreet Technologies & Data Services; Chennai District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Dun & Bradstreet Technologies & Data Services in Chennai invites a Data Scientist to design, implement and validate credit scoring models using ML and statistical techniques on financial and alternate data.

You will own the model lifecycle, collaborate with engineering for API and batch scoring, build monitoring dashboards, and produce validation reports for stakeholders. Strong Python, SQL, and knowledge of .NET/C# is expected; Power BI familiarity is a plus.

Qualifications

• 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
• Strong communication skills

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.
• Drive credit risk strategy through robust, production-grade models
• Improve portfolio performance and decision accuracy
• Shape best practices in model development, deployment, and monitoring

Skills

ML model implementation

Python

SQL

C# / .NET

Model validation

Communication

Project management

Tools

Power BI

SAS

STATA

Oracle

PostgreSQL

SQL Server

MongoDB

Job description

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
• Strong 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