Specialist, Gen AI Solutions

• Use Case Formulation & Solution Design
• Collaborate with Risk and Compliance stakeholders to translate business problems into clearly defined analytics/ML use cases with measurable outcomes.
• Conduct feasibility assessments, including data availability, model ability, risks, and expected value.
• Develop solution designs and model blueprints, including data pipelines, features, algorithms, controls, and monitoring approach.

Business

• Identify opportunities to enhance automation and alert quality (reducing false positives/negatives).

Key Responsibilities

Processes

• Design, build, and validate statistical and machine learning models for areas such as:
  • Credit risk scoring and early warning indicators
  • Financial crime (AML, fraud detection, sanctions screening enhancements)
  • Conduct risk and compliance surveillance
  • Operational and cyber risk analytics
• Implement models using appropriate tools and frameworks (e.g., Python, R, SQL, Spark) following the bank’s model risk and development standards.
• Ensure models are explainable, auditable, and compliant with model governance, regulatory expectations, and ethical AI principles.
• Partner with Technology/Data Engineering to productionize models and integrate them into business workflows and platforms.
• Source, explore, and assess the quality of internal and external datasets relevant to risk and compliance.
• Design and implement robust data preprocessing, feature engineering, and variable selection pipelines.
• Ensure data usage adheres to data privacy, security, and governance standards.

People & Talent

• Lead through example and demonstrate the bank’s culture and values

Key Responsibilities

Risk Management

• Perform initial root cause analysis using logs, metrics, and runbooks.
• Coordinate with Data Engineering, Model Development, IT Operations and Business/Risk owners to remediate issues.
• Log incidents, track actions, and ensure timely closure in line with SLAs.
• Escalate material issues in accordance with risk, compliance and operational incident frameworks

Governance

• Define, implement, and document model performance metrics and validation approaches (e.g., accuracy, stability, fairness, drift).
• Support internal model validation, audit, and regulatory reviews by providing clear documentation and technical explanations.
• Design monitoring dashboards and processes to track model performance, drift, and usage; proactively recommend recalibration or redevelopment where needed.
• Ensure adherence to model risk policies, governance frameworks, and regulatory guidelines for AI and advanced analytics.

Key Responsibilities

Reporting & Stakeholder Communication

• Present analytical insights and model outcomes in a clear, non‑technical manner to senior stakeholders in Risk, Compliance, and Business.
• Collaborate with Operations, Technology, and Change teams to embed models into processes, controls, and decisioning systems.
• Provide training and guidance to Risk and Compliance teams on the use and limitations of analytical models and AI tools.