ML Validator, Model Risk and Analytics Division

About the role

The incumbent will be responsible for validating, monitoring, and improving machine learning models across the bank, ensuring data quality, model reliability, explainability, and compliance with Model Risk Management standards.

Key responsibilities

• Prepare and ensure data quality control measures for training and validation of ML models in various business processes.
• Develop methods, principles, and automated systems for validation and testing of machine‑learning micro‑services.
• Design and maintain model risk management processes and model quality monitoring systems.
• Improve business analytics and validation methodology for models and report validation results to stakeholders.

Required profile

• University degree in statistics, mathematics, risk management, engineering or related field.
• Strong knowledge of statistics, probability, financial analysis and machine‑learning models.
• Understanding of interpretability for statistical and deep‑learning models, including data, model and concept drift.
• Good communication, analytical and creative thinking skills; fluent in Armenian and English.

Required skills

• Python development (including poetry, pip, Git and Docker).
• Experience with model interpretability frameworks such as SHAP and ELI5.