• Oversee, build, improve Credit Risk engine so it could be proudly recognised as best in class (framework, guidance, analytics and technical setup)

• Own and develop the end-to-end credit risk framework for credit cards, including eligibility, underwriting, scoring models, limit setting, risk appetite, monitoring, and reporting

• Partner closely with Product and Engineering to embed risk into product design and strategy from day one

• Architect the data strategy: Transactional data, multi-country bureau landscape, behavioural data , accounting software, government registry data and first party transactional signals on accounts. Guide the ML & DS team in building, validating, and iterating credit scoring and related risk models

• Build and own the portfolio monitoring stack,

• Design, build and calibrate the early warning indicator / credit monitoring system : transaction velocity, balance deteriorating, payment pattern changes, supplier/customer/revenue concentration shift, signals from the external data sources

• Act as a strong Second Line of Defense (2LoD), ensuring robust origination, monitoring, and collections processes

• Own and continuously improve credit risk policies, risk appetite frameworks, and reporting

• Ensure alignment with EMI / PS4 regulatory requirements and evolving European standards

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• 5–7+ years of experience in credit risk, with hands-on expertise in SME lending and credit cards

• Strong experience with automated underwriting and risk models in fintech or digital banking environments

• STEM/quantitive degree: physics, data science, applied mathematics, statistics, econometrics or quantitive finance is a strong plus.

• Solid understanding of risk governance, policies, and processes

• AI-native mindset — comfortable leveraging AI tools and agents to enhance productivity and decision-making

• Unit-economics/ product led growth mindset (eligibility, conversion, expected lose, cost of risk, false-positives as missed revenues, risk-adjusted margin), not just control

• Highly collaborative, with the ability to align diverse stakeholders (Product, Legal, Finance, Engineering, ML/DS)

• Analytical, structured, and pragmatic — able to combine deep detail with big-picture thinking

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