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Senior Risk Analyst - FinTech (7-8 yrs)
- Risk management: participation in activities affecting risk level and LTV, regular review, development and coordination on risk-decisions.
- Monitoring on portfolio quality: checking existing and expected risk level, catching anomalies.
- Provisioning policy: monitoring and analysis on provisioning level (PD, LGD, EAD).
- Monitoring on risk-model performance: daily risk-filter monitoring, weekly-, monthly- reportment, score-model performance control.
- Risk-events investigations: fraud attack detection, IT-down catching (operational risks).
- Credit policy owner: development, actualizing, AB-test managing, updating. Matching with risk-engine specification.
- Risk-events history keeper: fixating and owing of company history of events affecting/explaining on risk level (incl seaonability, special campaigns, ne model implementation, AB-tests, etc. )
- Change management: participating, control and managing on full change implementation process ( change request - task clarification - development support - quality testing - production testing - final decision on using as - champion- )
- Risk-modeling: risk-kpi assessment, kpi-based decisions foundation.
- Business level risk-modeling: development on logical risk-rules and antifraud-rules, monitoring on social fraud information. Common with score-modeler - business understanding and expertise on main variables (top features) and correlations in score model.
- Existent + new data-sources: comparing, modeling on effect, assessment, trial-using coordination, integration managing.
- Risk cost management: assessment on profitability from risk-activities, data-sources costing-model optimization.
- Risk budgeting: participation in company budgeting from risk prospective - forecasting of provisions, approval-rate, default level, risk-model and collection costing.
- Team support: risk consulting, common projects, especially for verification and collection teams.
Technical Skills Required:
- Excel - advanced
- SQL: as business-user (basic and data-understanding level)
- Score modeling experience (any type of environment - R, Python, SS, SPSS, ets.)
- Math, tech education (ML, math statistics, probability theory)
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