Senior Credit Analyst

The Senior Credit Analyst role works in a supervisory capacity to the Credit Risk Department, overseeing two units: Research Analysis and Credit Investigation. The role will focus on helping develop credit risk models, scorecards, and portfolio analytics that can support better lending and portfolio management decisions.

Credit Investigation:

1. Implement Research Unit Insights to the day-to-day pipeline for underwriting business loans.
2. Ensure compliance with internal policies, KYC standards, and regulatory requirements.
3. Provide initial approval decisions and structure credit offers for business loan applications.
4. Regularly evaluate efficiency of the approval process and coordinate improvements as needed.

Data & Modeling:

1. Work with customer, financial, transaction, and lending data to better understand borrower behavior.
2. Clean, prepare, and analyze data using Python and SQL.
3. Explore datasets and identify patterns, trends, and variables that could be useful for predicting credit risk.
4. Help build and test statistical and machine learning models.
5. Support the development of credit scores, risk segments, and scorecards.
6. Test different modeling approaches and evaluate how well they perform.
7. Help with feature engineering, model validation, and model monitoring.
8. Investigate issues such as missing data, data leakage, bias, overfitting, and changes in model performance over time.

Portfolio Analytics:

1. Analyze the lending portfolio across different customer segments, industries, products, and risk levels.
2. Monitor portfolio performance, including delinquencies, defaults, exposures, and losses.
3. Identify trends or segments where credit risk may be increasing.
4. Look at how changes in customer mix, underwriting policies, or approval criteria could affect overall portfolio risk.
5. Help develop metrics and analyses for tracking portfolio health.
6. Help answer questions like:

• Which types of borrowers are performing better or worse than expected?
• Where are we seeing higher concentrations of risk?
• How is the portfolio changing as we grow?
• What happens to portfolio risk if we change our approval criteria?
• Are our risk models still performing well as the portfolio changes?

Business & Decision Support:

1. Work with business and risk teams to understand the lending process and how analytical models will actually be used.
2. Help translate analytical findings into practical recommendations.
3. Think about the trade-offs between growth, credit quality, and risk.
4. Present findings clearly to both technical and non-technical stakeholders.
5. Help connect individual borrower-level analysis to the bigger picture of portfolio performance and business strategy.

Requirements:

1. Around 2–3 years of experience in data analytics, data science, risk analytics, or a similar role.
2. Good working knowledge of Python, particularly pandas, NumPy, and scikit-learn.
3. Comfortable working with SQL.
4. Good foundation in statistics and machine learning.
5. Experience actually building and evaluating models.
6. Strong analytical and problem-solving skills.
7. Comfortable working with messy or incomplete data.
8. Able to work independently and figure things out when the problem isn't clearly defined.
9. Good communication skills and the ability to explain technical concepts in a simple way.
10. Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, Physics, or another quantitative field.

Nice to Have:

1. Experience in lending, credit risk, banking, fintech, insurance, or financial services.
2. Experience working with credit or risk models.
3. Familiarity with concepts such as probability of default, default rates, risk segmentation, scorecards, WoE/IV, and model monitoring.
4. Experience with portfolio monitoring or portfolio analytics.
5. Experience working with financial or transactional data.
6. Experience working with imbalanced classification problems or time-based validation.
7. Experience with SME, commercial, or business lending.
8. Knowledge of delinquency, default, probability of default, and portfolio risk concepts.
9. Experience with forecasting, customer segmentation, or predictive analytics.