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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in credit risk modeling, including PD, LGD, and EAD, with a strong focus on IFRS 9 ECL modeling and Basel regulatory compliance. Proficient in statistical modeling, machine learning techniques, and large-scale data analysis to drive insights and enhance credit risk strategies.
Highest-signal resume keywords
Credit Risk Modelling TechniquesIFRS 9 ECL ModellingStatistical ModelingSAS, SQL, Python/RModel Lifecycle Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Credit Risk ModellingStatistical ModelingMachine Learning TechniquesData AnalysisModel ValidationModel MonitoringBacktestingModel RedevelopmentData IntegrityPortfolio Management
Soft Skills
CommunicationMentoringCollaborationAnalytical ThinkingProblem Solving
Tools & Technologies
SASSQLPythonRHadoopMLOps StandardsDecision SystemsRating PlatformsStatistical SoftwareData Platforms
Certifications & Qualifications
FRMCFA
Industry Keywords
Credit RiskBasel FrameworksRetail BankingFinancial ServicesPortfolio OptimizationRisk SegmentationRegulatory ComplianceModel GovernanceData DriftClimate Risk
Tech Stack
Tools & technologiesHadoopPythonSQL
About the role
Key responsibilities & impact- Responsible for the development, implementation, and maintenance of credit risk models and scorecards, including PD, LGD, and EAD across the retail portfolio lifecycle (acquisition, behavioral, collections).
- Lead the design and enhancement of credit risk modelling frameworks, incorporating scorecards and appropriate statistical/analytical techniques to support underwriting and portfolio management decisions.
- Monitor, document, and communicate the performance, assumptions, and limitations of credit risk models to stakeholders, ensuring transparency and model interpretability.
- Perform model monitoring, backtesting, and periodic recalibration, ensuring models remain accurate, stable, and compliant over time.
- Provide recommendations for model redevelopment or enhancement based on portfolio trends, data drift, and emerging risk patterns.
- Prepare and support Basel regulatory reporting, including RWA estimation and model-related submissions aligned with internal and regulatory requirements.
- Lead/support IFRS 9 ECL modelling, including staging, macroeconomic overlays, scenario-based expected credit loss estimation and stress testing including climate risk.
- Deploy credit risk models into production systems / rating platforms, working closely with IT and data teams to ensure data integrity and system robustness.
- Support design and implementation of credit risk strategies and decision rules (e.g., cut-offs, risk segmentation, line management) aligned with model outputs.
- Perform and oversee model validation and testing activities (functional, statistical, and regulatory) prior to deployment.
- Establish robust model governance practices, including documentation, audit trails, and compliance with regulatory standards.
- Identify opportunities to enhance credit risk models using advanced analytics or machine learning techniques, where appropriate and justifiable.
- Establish MLOps standards for model deployment, monitoring, versioning, and performance tracking in production environments.
- Develop data-driven insights to monitor portfolio quality, risk trends, and early warning indicators.
- Collaborate with policy, finance, and business teams to support portfolio optimization, provisioning, and capital management decisions.
- Ensure timely communication of model performance, validation findings, and risk insights to senior management and committees.
- Mentor junior analysts and contribute to building a strong, technically sound credit risk modelling team.
Requirements
What you’ll need- 10–12+ years of experience in credit risk modelling within retail banking / financial services
- Deep expertise in statistical modeling, machine learning techniques, and large-scale data analysis.
- Strong expertise in credit risk modelling techniques, including PD, LGD, EAD, scorecards, and segmentation approaches
- Proven experience in IFRS 9 ECL modelling and Basel frameworks
- Strong knowledge of model lifecycle management (development, validation, deployment, monitoring)
- Advanced technical skills in SAS, SQL, and Python/R
- Experience in working with large datasets and data platforms (e.g., Hadoop or equivalent)
- Strong statistical and analytical skills with ability to translate data into insights
- Proven track record of building, deploying, and maintaining production ML models with real-time or near-real-time decisioning systems.
- Familiarity with decision systems / rule engines is an advantage
- Professional certifications such as FRM (Financial Risk Manager) or CFA (Chartered Financial Analyst) are a strong plus
- Degree in Quantitative disciplines (Statistics / Mathematics / Actuarial Science / Economics)
Benefits
Comp & perks- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities