Key Responsibilities

• Perform ad-hoc analysis on large-scale data to extract actionable insights and support data-driven decision making.
• Develop a strong understanding of the credit risk modelling lifecycle, including data preparation, model development, validation, and monitoring.
• Build foundational knowledge of machine learning techniques and their applications in credit risk — including classification, regression, and ensemble methods.
• Support validation of existing credit risk models and assist in developing or enhancing models under the guidance of senior team members.
• Extract, manipulate, and explore large datasets using Python (pandas and related data analysis libraries) and SQL to ensure data quality and integrity.
• Communicate analytical findings and model results clearly to both technical and non-technical stakeholders, including leadership presentations

Required Skills & Experience

• Upto 4 years of experience in data science, credit risk analytics, or a related quantitative role.
• Hands-on proficiency in Python — particularly pandas, NumPy, and data analysis libraries — for data manipulation and exploratory analysis.
• Working knowledge of SQL for data extraction and querying large datasets.
• Foundational understanding of machine learning concepts and modelling techniques (e.g., logistic regression, decision trees, gradient boosting).
• Exposure to credit bureau data, credit risk concepts, or consumer lending data is a plus.
• Strong communication skills with the ability to present analysis and findings clearly to both technical teams and leadership.

Preferred / Additional Skills

• Familiarity with data visualization tools such as Power BI for presenting analytical insights.
• Basic awareness of cloud-based data platforms (AWS) and big data tools.
• Prior academic or project experience with credit scoring, risk modelling, or financial data analysis.

Educational Qualifications

• Degree from a top-tier institution; Particularly in a quantitative field (e.g., computer science, data science, engineering, economics, mathematics, etc.). Advanced degree preferred.