Senior Specialist: Data Scientist

The Senior Specialist: Advanced Analytics will lead Postbank’s advanced analytics and data-science capability for assigned enterprise domains. In this role, you will define analytical standards, govern the advanced analytics portfolio, and translate strategic and regulatory priorities into executable analytics roadmaps. You will provide authoritative leadership over high-impact models, data-science solutions, model-risk controls, stakeholder advisory, and the performance delivery of Data Scientist resources.

Job Responsibilities

Advanced Analytics Strategy, Portfolio, and Standards

• Translate Postbank priorities into a comprehensive advanced analytics roadmap, delivery portfolio, and standards for assigned domains.
• Prioritize 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 of Analytics and senior stakeholders on analytics opportunities, trade-offs, dependencies, risks, and investment requirements.
• Lead the adoption of scalable analytics methods, platforms, and ways of working that improve delivery quality and speed.

Enterprise Solution Leadership and Delivery

• Lead complex predictive, prescriptive, segmentation, forecasting, optimization, and machine-learning initiatives from concept to implementation handover.
• Approve analytical design choices, modeling 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 strategic 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 strict 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, and assurance evidence relating to advanced analytics outputs.
• Escalate material risks and recommend control improvements to the Head of Analytics.

People, Resource, and Capability Leadership

• Lead, allocate, and monitor the 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 the continuity of critical analytics workstreams.
• Promote a professional, inclusive, and high-accountability delivery culture aligned with Postbank values and governance obligations.

Qualifications and Experience

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, or a relevant field (NQF Level 7).
• Honours or Master’s degree in a relevant field (NQF Level 8/9) (Ideal).
• 8+ years of relevant data science, machine learning, and algorithm-related experience at a specialist level.
• 5+ years of managerial and team-leading experience.
• Experience in the banking industry (Advantageous).

Skills and Attributes

• 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.
• Self-motivation and the ability to operate independently.
• Continuous learning and intellectual curiosity.
• Loyalty to team objectives and organizational success.

Technical Stack and Target Skills by Phase

Phase 1: Core Data and BI Engineering

• Database optimization, automated ETL pipelines, and stored procedures using SQL Server (SSMS, SSIS) and Power Query.
• Advanced data modeling, context filtering, row-level security, and cloud deployment using Power BI (DAX) and Tableau.
• Statistical profiling, macro migration, and legacy predictive validation using SAS.

Phase 2: Predictive AI and Automation

• Binary classification and feature engineering for upsell and cross-sell propensity modeling using Python (scikit-learn, XGBoost), R, and SQL Server.
• Regression trees, ensemble learning, and automated lead generation scoring engines using Python and SQL Server.
• Survival analysis, lifetime value (LTV) estimation, and NLP for service ticket intent using Python (lifelines, HuggingFace), R, and Power BI.

Phase 3: Advanced Optimization and Ecosystem Transformation

• Location allocation algorithms, spatial clustering, and spatial regression using ArcGIS, Python (GeoPandas, PySAL).
• Constrained optimization, attribution modeling, and marketing mix modeling (MMM) using Python (SciPy.optimize) and R.
• Bridging deep tech with executive business decisions and setting enterprise architectural patterns.

How to Apply

If you wish to apply and meet the requirements, please forward your Curriculum Vitae (CV).