Data Scientist

The Data Scientist is accountable for independently designing, developing, validating and ope rationalising advanced analytics and machine-learning solutions for assigned business problem areas. The role translates complex business and regulatory questions into 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.

• Own assigned advanced analytics work streams 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.

• 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.

• 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.

• 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.

• Bachelor’s degree in Mathematics, Statistics, Applied Mathematics, Data Science, Engineering, Economics, or a related quantitative field (NQF Level
  7).
• Honours or Masters degree in a quantitative, data science, analytics or related field (NQF Level 8/9 (Ideal)
• 5+ Years Relevant data science, machine learning and algorithm-related experience at a specialist level
• Experience in the Banking Industry (Advantageous)

A phased skills pathway for strengthening data engineering, business intelligence, predictive AI, automation, optimisation and strategic analytics capability.

• Data Pipeline Modernization: Database optimization, automated ETL pipelines and stored procedures.
• Advanced Business Intelligence: Advanced data modeling, context filtering, row-level security and cloud deployment.
• Enterprise Analytics Legacy Integration: Statistical profiling, macro migration and legacy predictive validation.

• Beh…