About the role
We are looking for a Data Analyst/Senior Data Analyst with strong expertise in credit risk analytics, predictive modeling, and statistical analysis to develop and optimize risk models using large-scale financial datasets. The ideal candidate will have hands-on experience in Python, PySpark, feature engineering, credit risk scorecards, and machine learning, with the ability to translate complex data into actionable business strategies and measurable financial outcomes.
Responsibilities:
- Data Analytics and Engineering: Clean, process, merge, and analyze large-scale financial datasets to develop high-quality analytical solutions and credit risk models.
- Feature Engineering: Identify, create, and select meaningful variables from financial and customer data to improve predictive model performance.
- Credit Risk Modelling: Develop, validate, and optimize application and behavioral scorecards using statistical and machine learning techniques.
- Model Deployment and Monitoring: Support model deployment, track performance, conduct backtesting, and recalibrate models to maintain accuracy and reliability.
- Business Strategy and Insights: Translate analytical findings into actionable recommendations for credit policies, credit limit optimization, delinquency management, and portfolio profitability.
- Model Governance and Compliance: Ensure models meet internal governance standards, regulatory requirements, and expectations for transparency and traceability.
- Stakeholder Management: Collaborate with business, risk, and analytics teams to communicate model performance, analytical insights, and recommendations.
Key Success Metrics:
- Improve credit risk model accuracy and predictive performance.
- Support better credit decisions and reduce portfolio default risk.
- Drive portfolio profitability through data-backed strategies.
- Deliver reliable, scalable, and well-governed analytical models.
- Continuously improve model performance through monitoring and recalibration.
Requirements:
- Proven experience in developing and deploying credit risk models (e. g., application scoring, behavioral scoring).
- Solid understanding of credit risk assessment techniques, including statistical, machine learning, and traditional risk modeling approaches.
- Experience in using data science and statistical software (e. g., Python, PySpark) for data analysis and model development.
- Strong communication skills, with the ability to present complex analytical results to non-technical stakeholders.
- Experience in model deployment practices.
Preferred Qualifications:
- Full-time bachelor's or master's degree in engineering, computer science, statistics, mathematics, economics, finance, or related disciplines.
- Relevant professional experience in credit risk analytics, scorecard development, statistical modelling, or predictive analytics.
- Experience working with banking or financial datasets.
- Strong communication and stakeholder management skills.