Senior Officer - Risk Data Management

Role: Senior Officer - Risk Data Management

Location: Abu Dhabi

Role purpose:

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Prepare, derive and validate variables and datasets required for the development, recalibration, validation and implementation of Application Scorecards and Behavioural Scorecards.

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Provide the Risk Modelling team with accurate, model-ready datasets for A-Score and B-Score development.

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Support accurate implementation of approved scorecards in production as part of the model implementation exercise.

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Automate BAU activities, including ingesting multi-source data in an agreed format and loading it into the Azure/CML database.

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Develop SAS-based automation on the existing SAS Risk Stratum engine and deploy ad-hoc changes as requirements arise.

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Coordinate with Model Development, Model Validation, Business and IT teams to resolve data and implementation issues, escalating where required.

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Run the existing SAS suite that loads LGD model data, including loss, recovery and post-default event data from source systems.

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Prepare data for CBUAE regulatory reporting and BRF requirements.

Key accountabilities of the role:

Scorecard Variable & Data Preparation:

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Prepare, derive and validate variables and analytical datasets required for Application and Behavioural Scorecard development, recalibration, validation and implementation.

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Interpret approved business definitions, model specifications and variable logic and convert them into controlled SAS and SQL programs.

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Consolidate source data, perform data-quality checks and deliver complete datasets in the agreed structure and format.

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Investigate data gaps, anomalies and reconciliation differences and coordinate their resolution with relevant stakeholders.

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Prepare data for CBUAE regulatory reporting and BRF requirements.

Model Implementation & Testing:

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Support model implementation by providing accurate insights into the underlying data and model logic.

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Perform unit testing, system integration testing, user acceptance support, reconciliation and parallel-run validation.

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Deploy SAS models to the IFRS 9 risk engine using SAS DI jobs, and benchmark results across the Development and UAT environments.

SAS Engineering & Data Management:

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Develop, maintain and optimise SAS and SQL programs used for variable preparation, scorecard implementation and recurring data processing.

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Support model deployment using SAS Data Integration, SAS Studio and related SAS platforms.

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Develop controlled data pipelines and maintain data lineage between source systems, SAS processes, modelling databases and downstream solutions.

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Execute relevant business-as-usual activities and provide technical support for implemented models.

Automation & Continuous Improvement:

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Automate recurring data preparation, validation, reconciliation and model implementation activities.

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Review and optimise existing SAS implementation code to improve efficiency, maintainability and control.

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Identify and implement SAS-based automation opportunities to reduce manual intervention and support the Provisioning, CBUAE Sup-Tech Risk Model BRFs.

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Support other risk model implementation and regulatory data automation activities where required.

Specialist skills / technical knowledge required for this role:

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Strong hands-on expertise in SAS programming, SAS Data Integration, SAS Studio and SQL.

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Practical experience preparing and deriving variables for Application and Behavioural Scorecards.

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Experience implementing scorecards or risk models, including testing, reconciliation and production deployment.

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Good understanding of data quality, data lineage, reconciliation, model implementation controls and documentation.

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Ability to review and optimise SAS code and develop efficient, controlled data pipelines.

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Working knowledge of relational database management systems and Oracle.

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Exposure to Python or SAS for analytics and automation.

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Strong analytical, problem-solving, documentation, communication and stakeholder-management skills.

Previous experience required (if any):

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Minimum 3–5 years of relevant experience in SAS development, risk data management, scorecard/model implementation, analytics or automation within banking or financial services.

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Demonstrated hands-on experience preparing variables and datasets for Application or Behavioural Scorecards.

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Experience translating approved model specifications into SAS- or SQL-based implementation logic.

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Experience working with Model Development, Model Validation, Business and IT stakeholders on controlled production implementation.

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Experience maintaining data-quality reports, lineage, implementation documentation and test evidence.

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Experience automating recurring modelling, data-management or risk-reporting processes.

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Experience with SAS Viya, Python, SAS MRM would be an advantage.