Senior Data Scientist

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