Senior Data Analyst
Summary
Full-time Senior Data Analyst/Data Scientist in Mumbai focused on credit risk: analyzing large datasets, building and validating credit risk models (PD/LGD/EAD), and doing portfolio and risk analytics to support lending decisions. Core stack is Python and SQL, with statistical/ML modelling and Power BI or Tableau as a plus.
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.
