Job Description

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Role: Manager - Risk Data Management
Location: Abu Dhabi
Role Purpose:
The role requires very strong technical and communication skills.
Main responsibilities include:

• Manage risk data including data capturing, organizing, storing, and analyzing for development, validation and implementation of Credit Risk Models and Scorecards.
• Provide advanced quantitative analytics support to the overall Risk Management function.
• Formulating management techniques for quality data collection to ensure adequacy, accuracy, and legitimacy of data.
• Ensure implementation of risk rating models and scorecards in compliance with IFRS 9 and Basel requirements.
• Engaging in Risk Architecture projects, including implementation of various risk IT applications, data analytics, data flows, standards, and processes.
• Develop and implement AI analytics to analyze risk data, use machine learning techniques to identify patterns, trends, and potential risks including exploration data analysis and data quality trend assessment
• Ensure all data management and AI analytics practices comply with relevant regulations and organizational policies
• Project management with ability to execute plans and meet project objectives as well as the ability to respond and resolve diverse needs of team. Must be detail-oriented and able to multitask.
• Strong knowledge of Information Technology systems, Risk Management systems, tools, applications, and relational database management system, software development and ability to code.
• Role requires independent thinking, strong communication, initiative, interaction with all stakeholders within the bank.

• Manage and maintain data for risk models implementation and scorecards development.
• Devising and implementing efficient and secure procedures for data management and analysis with attention to all technical aspects.
• Liaise and engage with external vendors and IT team for project implementation.
• Good quantitative analysis and finance skills, experience of working with Statistical Software (SAS/Python/R).
• Knowledge of Data Analytics and reporting tools and software used (Power BI, SAP BusinessObjects, SQL Server).
• Perform data analytics for modelling teams in development, recalibration and validation of Risk and statistical models by carrying out data preparation, segmentation analysis, and testing tasks. Assist with data extraction when needed.
• Apply techniques such as generative AI, classification, regression, clustering, and other related methods.
• Troubleshoot data-related problems and authorize maintenance or modifications.
• Ensure data quality review and standard checks to assess the data completeness, accuracy, timeliness, uniqueness and traceability.
• Provide analytics and quantitative support as and when necessitated.
• Implement data tracking and monitoring tools, analyze and validate data, ensuring data security.
• Manage reporting for senior management or regulator.
• Team player and leader with the ability to work very effectively in a collaborative and team-oriented manner with other team members.
• A positive work attitude and confident, eager, and willing to take on new challenges. Must be cooperative with good interpersonal, leadership, negotiation, and conflict resolution skills. Self-motivated, detail-oriented, should exhibit problem solving skills as well as ability to deliver under pressure.
• Willing to take on additional responsibilities and tasks as the needs arise.
• To perform any other responsibility assigned by Head Enterprise Risk Management Group from time to time.

• Minimum 5+ years of total experience in handling data management projects, data science, machine learning, model development within banking and finance sector, preferably in Credit Risk domain i.e., Basel II and IFRS 9, Risk models development and implementation, risk analytics, reporting
• Bachelor's degree in computer science, Engineering, Information Systems, or a related field.
• Master's degree is preferred

• Excellent Credit Risk modelling, analytical, and research skills.
• Experience working with large and complex data sets, including alternative data (bureau, open banking etc.) for credit models.
• Good knowledge of financial markets and products and aware of the latest analytical techniques including ML algorithms such as SVM, Random Forest and Gradient Boosting etc.
• Possess good knowledge of credit risk management best practices including but not limited to pertinent Basel II, Basel III and IFRS 9 Framework on expected credit risk loss, credit risk management and capital adequacy requirements.
• Possess strong quantitative skills and experience in developing, validating, and monitoring risk models. Knowledge of the credit scoring systems available in the market and their use.
• Advanced users of statistical tools and programming languages (such as SAS, R, Python and SQL)
• Should have a strong knowledge of handling Risk Technologies & its implementation.
• Apply techniques such as generative AI, classification, regression, clustering, and other related methods.
• Ability to work independently on multiple tasks and/or projects.
• Excellent oral and written communication skills in English.
• Proficiency in risk concepts, banking products/ operations/ systems, pertinent regulatory requirements,
• Flexible team player and able to work and deliver under pressure.