SENIOR AI AND ML SPECIALIST

Bachelor’s degree in Mathematics, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, Statistics, Applied Mathematics, or a related quantitative/technology field (NQF Level 7). Honours or Masters degree in AI, machine learning, data science, computer science, analytics or related field (NQF Level 8/9) (Ideal) 8+ years Relevant Artificial Intelligence and Machine Learning-related experience at a Specialist level 5+ years Managerial / Team Lead experience Relevant experience in the Banking Industry (Advantageous) Uncompromising integrity, honesty, and professional ethics. Respectful and professional conduct at all times. Strong personal accountability and ownership. A commitment to excellence in every deliverable. High levels of resilience and perseverance when facing obstacles. Page | 1 Self-motivation and the ability to operate independently. Continuous learning and intellectual curiosity. Loyalty to team objectives and organisational success. Phase Target Skills & Algorithms Tech Stack Mapping Database optimization, automated ETL SQL Server (SSMS, SSIS), Power Query pipelines, stored procedures. 1. Core Data & Advanced data modeling, context filtering, row- Power BI (DAX), Tableau BI Engineering level security, cloud deployment. Statistical profiling, macro migration, legacy SAS predictive validation. Binary classification, feature engineering for Python (scikit-learn, XGBoost), R, SQL Server upsell/cross-sell propensity modeling. 2. Predictive AI Regression trees, ensemble learning, automated Python, SQL Server, Power & Automation lead generation scoring engines. Query Survival analysis, lifetime value (LTV) Python (lifelines, HuggingFace), R, Power BI estimation, NLP for service ticket intent. Location allocation algorithms, spatial clustering, ArcGIS, Python (GeoPandas, PySAL) spatial regression. 3. Advanced Constrained optimization, attribution modeling, Optimization & Python (SciPy.optimize), R marketing mix modeling (MMM). Geo Bridging deep tech with executive business Full Ecosystem Transformation decisions, setting architectural patterns.

AI/ML Strategy, Portfolio and Governance Translate Postbank priorities into an AI/ML roadmap, standards and delivery portfolio for assigned domains. Prioritise AI/ML initiatives based on strategic value, feasibility, risk, data readiness, regulatory implications, resource availability and operational benefit. Define standards for responsible AI, solution design, model development, validation, explainability, deployment, monitoring, security and retirement. Advise the Head: Analytics and senior stakeholders on AI opportunities, constraints, risks, investment needs and implementation options Evaluate new AI technologies, tools, vendors and platforms and recommend adoption, containment or rejection based on value and risk. AI/ML Solution Leadership and MLOps Lead complex AI/ML, deep-learning, NLP, optimisation, automation or generative-AI initiatives from concept to deployment handover. Approve or challenge AI architecture, algorithm choices, MLOps design, testing strategies, monitoring thresholds and integration approaches. Coordinate implementation dependencies with technology, BI, automation, data, risk, operations, compliance and business stakeholders. Oversee design and maintenance of MLOps practices for secure, scalable, repeatable and auditable AI/ML delivery. Present AI/ML findings, solution options, benefits, limitations and risk implications to executive, operational and technical audiences. Responsible AI, Risk, Compliance and Auditability Own AI/ML governance artefacts, model registers, responsible-AI evidence, deployment documentation and monitoring evidence for assigned domains. Ensure compliance with POPIA, FSCA requirements, internal data-governance policies, information-security standards and model-risk controls. Oversee fairness, bias, explainability, privacy, robustness, security, drift and performance monitoring for AI/ML solutions Coordinate audit, assurance, regulatory or internal-control evidence relating to AI/ML solutions. Escalate material ethical, operational, security or compliance risks and recommend control improvements. People, Resource and Capability Leadership Lead, allocate and monitor work of AI and ML Developer resources in line with portfolio priorities, quality gates and deadlines. Set role expectations, review performance inputs, coach AI engineering capability and identify development needs Contribute to recruitment, onboarding, probation input, workforce planning and succession planning for AI/ML roles. Manage workload, continuity, leave planning and delivery risk across AI/ML workstreams Promote a professional, responsible, secure and innovation-oriented AI delivery culture aligned to Postbank values

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