Data Analyst

About the job

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.