About the Role
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The Senior Officer - Risk Data Management will play a critical role in preparing, deriving, and validating variables and datasets essential for developing, recalibrating, validating, and implementing Application and Behavioural Scorecards. The position involves automating business-as-usual activities, delivering accurate model-ready datasets, and supporting the implementation process in collaboration with various teams.
Key Responsibilities
Scorecard Variable & Data Preparation:
- Prepare, derive, and validate variables and analytical datasets for Application and Behavioural Scorecard development, recalibration, validation, and implementation.
- Interpret approved business definitions, model specifications, and variable logic; 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; coordinate their resolution with relevant stakeholders.
- Prepare data for regulatory reporting and BRF requirements.
Model Implementation & Testing:
- 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; benchmark results across Development and UAT environments.
SAS Engineering & Data Management:
- Develop, maintain, and optimize SAS and SQL programs for variable preparation, scorecard implementation, and recurring data processing.
- Support model deployment utilizing SAS Data Integration, SAS Studio, and related SAS platforms.
- Develop controlled data pipelines and maintain data lineage between source systems, SAS processes, modeling databases, and downstream solutions.
- Execute business-as-usual activities and provide technical support for implemented models.
Automation & Continuous Improvement:
- Automate recurring data preparation, validation, reconciliation, and model implementation activities.
- Review and optimize existing SAS implementation code for efficiency, maintainability, and control.
- Identify and implement SAS-based automation opportunities to reduce manual intervention and support the Provisioning and regulatory Sup-Tech Risk Model BRFs.
- Support automation activities for other risk model implementations and regulatory data where required.
Required Skills and Technical Knowledge
- Strong hands-on expertise in SAS programming, SAS Data Integration, SAS Studio, and SQL.
- Practical experience in preparing and deriving variables for Application and Behavioural Scorecards.
- Experience in 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 optimize SAS code; 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.
Experience Requirements
- 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 collaborating with Model Development, Model Validation, Business and IT stakeholders on controlled production implementation.
- Experience maintaining data-quality reports, data lineage, implementation documentation, and test evidence.
- Experience automating recurring modelling, data-management, or risk-reporting processes.
- Experience with SAS Viya, Python, and SAS MRM would be an advantage.
