Data scientist in model risk

Model Risk Management Department at Bank of Georgia is looking for passionate, curious, and talented Data Scientist to join our growing team . You will have an opportunity to contribute to various innovative data-driven products and scalable intelligent systems. Members of the team are testing, validating, challenging and monitoring all the AI/ML models and solutions developed/used in the bank in order to mitigate model risk and create safe environment for operations in different areas of business (credit risk, marketing, customer care, optimization of back-office operations, etc.).

• Independently lead model validations end-to-end, from scoping and planning to final reporting, assessing model performance, conceptual soundness, input data quality, and development methodology.
• Design and carry out model outcomes analysis, including back-testing, sensitivity analysis, and stress testing of model behavior.
• Evaluate new and existing models' overall fit and suitability with their intended use and purpose and constructively challenge model owners and developers on key design choices.
• Develop a thorough understanding of the business processes, products, and decision-making context in which models are used, and assess whether model design, inputs, and outputs are appropriate for that context.
• Identify models' key assumptions, limitations, and weaknesses, assess the effectiveness of compensating controls, rate the severity of findings, and formulate practical risk mitigation recommendations.
• Develop benchmark and challenger models, selecting appropriate techniques to substantiate the assessment of models under review.
• Prepare clear, well-structured validation reports and present findings and recommendations to model owners, developers, and business stakeholders.
• Apply the Model Risk Policy and related standards consistently in day-to-day work and contribute to their review and enhancement.
• Perform ongoing model performance monitoring, define appropriate metrics and thresholds, and follow up on the remediation of identified issues.
• Contribute actively to model governance activities and drive the optimization of the validation and monitoring process through automation and reusable tools, incorporating regulatory changes and other initiatives.
• Validate advanced AI/ML and Generative AI solutions (including LLM- and RAG-based applications) and contribute to developing the team's validation approaches for these model types.
• Mentor and support junior team members, review their work, and contribute to knowledge sharing within the team.

• Degree in Statistics, Mathematics, Data Science, Computer Science, or related fields.
• 1–3 years of relevant working experience in model development, model validation, data science, or quantitative analysis.
• Proven experience working with complex and large-scale structured datasets
• Strong knowledge of statistical modeling and machine learning techniques, including model evaluation, explainability, and bias/fairness assessment.
• Strong proficiency in Python and/or R and SQL; ability to write clean, reproducible, and well-documented code.
• Experience with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
• Experience with data visualization libraries (such as Matplotlib or Plotly) and tools (such as Microsoft Power BI or Tableau).
• Solid knowledge of relational database concepts and data modeling.
• Strong data visualization and storytelling skills; ability to independently design and build dashboards and monitoring reports.
• Ability to communicate complex analytical findings clearly and persuasively to both technical and non-technical audiences, including senior stakeholders.
• Strong analytical and problem-solving skills.
• Ability to analyze and understand business processes and translate business context into model risk considerations.
• Critical thinking, professional skepticism, and attention to detail.
• Team player and self-starter with a strong sense of ownership, able to manage multiple validations and priorities in parallel and deliver to deadlines.
• Ability to troubleshoot issues independently and exercise sound judgment on when to escalate.
• Willingness to mentor junior colleagues, provide constructive feedback, and continuously develop their own expertise.
• Proficiency in writing clearly and concisely in both Georgian and English.
• Будет плюсом: Experience in the financial sector, particularly banking (e.g., credit risk, IFRS 9, or scoring models), is a plus; experience with unstructured data (text, documents) is a plus; Practical experience with deep learning, reinforcement learning, or natural language processing is an advantage; Hands-on experience with Large Language Models (LLMs) and their practical applications, including prompt engineering, fine-tuning, or LLM evaluation, is an advantage; practical experience with transformer-based models is an advantage; Understanding of Retrieval-Augmented Generation (RAG) systems and their components, including vector databases, embedding models, and document retrieval pipelines, as well as approaches to evaluating them, is a plus; familiarity with Big Data technologies (Hadoop/Hive, Spark, NoSQL, etc.) is a plus

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