Analyst - Data Science

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.