Senior Data Analyst (Retail Liabilities)

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.

Skills

Analytics, Credit Risk, Data Analyst, PySpark, Python, Scorecard