SENIOR SPECIALIST: ADVANCED ANALYTICS (DATA SCIENTIST)

Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, or relevant field (NQF Level 7) Honours / Masters in relevant field (NQF Level 8 /9) (Ideal) 8+ years Relevant data science, machine learning and algorithm-related experience at a specialist level 5+ years Managerial / Team Leading experience 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. Page | 1 High levels of resilience and perseverance when facing obstacles. 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.

Advanced Analytics Strategy, Portfolio and Standards Translate Postbank priorities into an advanced analytics roadmap, delivery portfolio and standards for assigned domains. Prioritise analytical initiatives based on strategic benefit, regulatory need, operational urgency, data readiness, risk and resourcing constraints. Define reusable standards for problem framing, data preparation, model development, validation, documentation, monitoring and reporting. Advise the Head: Analytics and senior stakeholders on analytics opportunities, trade-offs, dependencies, risks and investment requirement Lead the adoption of scalable analytics methods, platforms and ways of work that improve delivery quality and speed. Enterprise Solution Leadership and Delivery Lead complex predictive, prescriptive, segmentation, forecasting, optimisation and machine-learning initiatives from concept to implementation handover. Approve analytical design choices, modelling approaches, validation methods, implementation assumptions and business interpretation for high-impact outputs. Coordinate cross-functional dependencies with BI, automation, data, technology, risk, operations and business stakeholders. Review and challenge Data Scientist outputs to ensure technical quality, business relevance, reproducibility and governance compliance. Present complex analytics findings, recommendations and options to executive, operational and technical audiences. Governance, Risk, Compliance, and Auditability Own the advanced analytics model register, documentation standards and evidence requirements for assigned domains. Ensure compliance with POPIA, FSCA requirements, Postbank governance frameworks, data-security controls and model-risk practices. Oversee model validation, explainability, bias testing, drift monitoring, performance thresholds, version control and retirement criteria Coordinate audit, internal control, regulatory or assurance evidence relating to advanced analytics outputs. Escalate material risks and recommend control improvements to the Head: Analytics. People, Resource and Capability Leadership Lead, allocate and monitor work of Data Scientist resources in line with portfolio priorities, deadlines and quality standards. Set role expectations, review performance inputs, coach technical capability and identify skills-development needs. Contribute to workforce planning, recruitment, onboarding, probation input and succession planning for advanced analytics roles. Manage workload, delivery risks, leave planning and continuity of critical analytics workstreams. Promote a professional, inclusive, high-accountability delivery culture aligned to Postbank values and governance obligations.

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