Senior Officer - Risk Data Management

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

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

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

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

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

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

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

Prepare data for CBUAE regulatory reporting and BRF requirements.

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

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

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

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

Prepare data for CBUAE regulatory reporting and BRF requirements.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Working knowledge of relational database management systems and Oracle.

Exposure to Python or SAS for analytics and automation.

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

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

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

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

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

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

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

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