Risk Analytics & Modeling Lead
On-siteAthens, Attica, Greece
Job Summary
Develop and own credit risk models, including scorecards, decision trees, and machine learning classifiers for PD, LGD, and EAD estimation aligned with CRR/CRD expectations. Build and maintain time-series models for portfolio monitoring, early warning signals, and stress testing per EBA Guidelines. Translate models into production-ready decisioning logic within loan origination and servicing systems while designing underwriting policies that ensure regulatory alignment. Conduct portfolio analytics, backtesting, and model performance monitoring covering discrimination, calibration, stability, and fairness. Support regulatory compliance through model governance, documentation, audit trails, and explainability under GDPR considerations. Collaborate with product, engineering, and data teams to embed risk logic into customer journeys across online and merchant channels. Operate in a hands-on capacity directly executing analyses, model builds, and implementations in a lean team environment.
Required Qualifications
- 7+ years in credit risk analytics, modeling, or quantitative risk within banking, fintech, or consumer finance
- Strong experience with both: Deterministic / rule-based decision frameworks (scorecards, policy rules)
- Strong experience with both: Machine learning methods for classification and time-series forecasting
- Deep understanding of loan mechanics (amortization, pricing, delinquency, recoveries) and unsecured lending products
- Proven experience with risk systems (LOS, decision engines, data pipelines) and translating models into production
- Solid knowledge of EU regulatory frameworks, including: EBA Guidelines on Loan Origination & Monitoring
- Solid knowledge of EU regulatory frameworks, including: Model risk management expectations (ECB/TRIM principles where relevant)
- Solid knowledge of EU regulatory frameworks, including: IFRS 9 impairment concepts
- Solid knowledge of EU regulatory frameworks, including: GDPR implications for automated decisioning
- Strong data skills (SQL, Python/R; experience with large datasets and feature engineering)
- Ability to operate independently and deliver end-to-end (from data extraction to model deployment)
Desired Qualifications
- Experience in embedded finance / BNPL / merchant-integrated lending
- Exposure to real-time decisioning systems
- Familiarity with alternative data and thin-file underwriting
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