Data Scientist

The Data Scientist is accountable for independently designing, developing, validating, and operationalising advanced analytics and machine-learning solutions for assigned business problem areas. This role translates complex business and regulatory questions into robust analytical approaches, makes interpretive technical decisions within approved governance frameworks, and produces auditable, production-ready analytical outputs that support operational, customer, financial, risk, and regulatory decision-making across Postbank.

Job Responsibilities

• Advanced Analytics Solution Ownership: Own assigned advanced analytics workstreams from problem framing to deployment-ready output; translate business problems into analytical hypotheses, data requirements, modelling approaches, and delivery plans; design and build statistical, machine-learning, and optimisation models using appropriate techniques and documented assumptions; determine suitable validation, back-testing, and monitoring approaches for assigned models and analytical outputs; convert model and analysis results into clear business recommendations, implementation options, and risk considerations for stakeholders.
• Data Engineering, Quality, and Reproducibility: Acquire, integrate, profile, and transform structured and unstructured data from approved sources; design repeatable data preparation, feature engineering, and analytical pipelines with appropriate quality controls; maintain version-controlled code, model artefacts, assumptions, metadata, outputs, and documentation for auditability and knowledge retention; identify data-quality, lineage, completeness, and integrity issues, and recommend corrective actions to the Senior Specialist.
• Model Risk, Governance, and Compliance: Apply Postbank data-governance, model-risk, POPIA, FSCA, and internal information-security requirements; conduct reasonableness checks, bias/variance assessments, explainability reviews, and performance monitoring for assigned analytical outputs; document model limitations, residual risks, and recommended monitoring controls before implementation or handover; support internal reviews, audit requests, and regulatory evidence packs relating to analytical models and outputs.
• Stakeholder Advisory and Delivery: Engage business, operations, BI, automation, data, and technology stakeholders to clarify requirements and implementation constraints; present analytical findings to technical and non-technical audiences in a clear, evidence-based manner; provide specialist input into analytics standards, reusable methods, reusable code libraries, and continuous-improvement initiatives; guide junior analysts or project contributors on technical methods where required, without formal people-management accountability.

Qualifications and Experience

• Bachelor’s degree in Mathematics, Statistics, Applied Mathematics, Data Science, Engineering, Economics, or a related quantitative field (NQF Level 7).
• Honours or Master’s degree in a quantitative, data science, analytics, or related field (NQF Level 8/9 ideal).
• 5+ years of relevant data science, machine learning, and algorithm-related experience at a specialist level.
• Experience in the banking industry is highly 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 organisational success.

Phase Target Skills and Algorithms Tech Stack Mapping

• Core Data and BI Engineering: Database optimization, automated ETL pipelines, stored procedures. Tech stack includes SQL Server (SSMS, SSIS), Power Query. Advanced data modeling, context filtering, row-level security, cloud deployment using Power BI (DAX), Tableau. Statistical profiling, macro migration, legacy predictive validation using SAS.
• Predictive AI and Automation: Binary classification, feature engineering for upsell/cross-sell propensity modeling using Python (scikit-learn, XGBoost), R, SQL Server. Regression trees, ensemble learning, automated lead generation scoring engines using Python, SQL Server, Power Query. Survival analysis, lifetime value (LTV) estimation, NLP for service ticket intent using Python (lifelines, HuggingFace), R, Power BI.
• Advanced Optimization and Geo: Location allocation algorithms, spatial clustering, spatial regression using ArcGIS, Python (GeoPandas, PySAL). Constrained optimization, attribution modeling, marketing mix modeling (MMM) using Python (SciPy.optimize), R. Bridging deep tech with executive business decisions and setting architectural patterns through full ecosystem transformation.

How to Apply

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