Senior Data Analyst

Experience: 35 Years
Location: [Mention Location]
Employment Type: Full-time

Role Overview

We are looking for a skilled Senior Data Analyst / Data Scientist with 3–5 years of experience in Credit Risk, Credit Risk Modelling, and Risk Analytics. The ideal candidate should have strong analytical and problem-solving skills, experience working with risk-related datasets, and the ability to develop data-driven insights and models to support credit risk assessment and business decision-making.

Key Responsibilities

• Analyze large datasets to identify trends, patterns, and insights related to credit risk and portfolio performance.
• Develop, validate, and monitor credit risk models for risk assessment and prediction.
• Work on credit risk modelling techniques, including Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD), wherever applicable.
• Perform risk analytics, portfolio analysis, customer segmentation, and creditworthiness assessment.
• Build and evaluate statistical and machine learning models to support credit risk management.
• Conduct data cleaning, feature engineering, exploratory data analysis, and model performance evaluation.
• Collaborate with business, risk management, and technical teams to translate business requirements into analytical solutions.
• Prepare reports, dashboards, and presentations to communicate insights and model outcomes to stakeholders.
• Monitor model performance and identify opportunities to improve model accuracy and risk assessment processes.

Required Skills and Qualifications

• 3–5 years of relevant experience in Data Analytics, Data Science, Credit Risk, or Risk Analytics.
• Hands-on experience in credit risk modelling and credit risk analytics.
• Strong proficiency in Python and SQL.
• Experience with statistical analysis, predictive modelling, and machine learning techniques.
• Understanding of credit risk concepts, risk scoring, credit underwriting, and portfolio risk assessment.
• Experience with data visualization tools such as Power BI or Tableau is an advantage.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience in banking, financial services, fintech, or lending domains.
• Knowledge of credit scoring models, Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).
• Familiarity with model validation, model monitoring, and regulatory risk frameworks.
• Experience working with large datasets and translating analytical findings into actionable business insights.