Data Scientist Credit Scoring & Analytics Implementation

CIEL HR; Chennai District; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

CIEL HR in Chennai seeks an experienced Data Scientist for Credit Scoring & Analytics Implementation. You will lead end-to-end model development, validation, deployment, and monitoring in production environments, applying ML and statistical methods to credit risk datasets.

You will collaborate with engineering and business teams to ensure scalable scoring logic, track model performance, and prepare comprehensive validation reports and stakeholder presentations.

Qualifications

• 8–10 years of experience in ML model implementation and credit risk solutions.
• Deep knowledge of logistic regression, WOE/IV, binning, and model validation.
• Proficient in Python; strong SQL and data handling on large databases.
• Experience integrating models into production systems and APIs.
• Excellent communication and stakeholder collaboration.

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.

Skills

Machine Learning

Statistical modelling

Model deployment

Documentation & reporting

Team guidance

Tools

Python

SQL

Oracle

SQL Server

PostgreSQL

MongoDB

SAS

STATA

.NET / C#

Power BI

Job description

Role: Data Scientist Credit Scoring & Analytics Implementation

Location:Chennai

Experience: 8-10 Years

CTC:15LPA to 20LPA

Notice period:Immediate to 15daysonly

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

• 5–10 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 asPower 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.