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Specialized credit risk modeling requirement reduces applicant pool despite metro location and mid-level seniority.
Role requires deep banking credit risk expertise, limiting cross-industry transferability.
Explicit years, IFRS9/domain experience, and mandatory tech stack create high shortlisting strictness.
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Develop, validate, and continuously improve credit risk models (e.g., IFRS 9, PD, LGD, EAD) supporting global financial services lending decisions.
Leverage AI/ML techniques and Google Cloud Platform (GCP) to enhance risk assessment, forecasting, and portfolio performance analytics.
Translate modeling outputs into actionable business insights, prepare presentations, and manage multiple stakeholder interfaces with accountability for project deliverables.
3 to 5 years of experience in banking or financial services with a focus on Credit Analytics.
Hands-on experience with credit risk model development and validation (IFRS 9, PD, LGD, EAD, credit scorecards, credit loss forecasting).
Proficiency in Python, SQL, SAS, R, GCP, and strong knowledge of predictive modeling and machine learning methodologies.
Master's degree in Finance, Financial Engineering, Analytics, Mathematics, Statistics, Computer Science, Economics, Industrial Engineering, Operations Research, or related quantitative discipline.
Experienced in deploying cloud-based analytical solutions, especially in Google Cloud Platform environments with AI integration to solve business challenges.
Strong understanding of end-to-end credit lifecycle and risk measurement within financial services credit risk management.
Demonstrated ability to manage complex projects independently, work effectively across multiple global stakeholders, and translate technical outputs into business insights.