Accountabilities (Business Outcomes)

Finance

• Data Engineering & Preparation:

  • Source, cleanse, and prepare both structured and unstructured data.

  • Develop reusable data models and maintain version-controlled datasets for ongoing analysis.

• Analytics & Machine Learning:

  • Design, build, and deploy predictive and prescriptive models for forecasting, anomaly detection, and process optimization.

  • Conduct profiling of stakeholders and transactional volatility.

  • Apply statistical techniques, cluster analysis, and AI algorithms (classification, regression, clustering, NLP, etc.).

  • Conduct exploratory data analysis (EDA) to identify trends, risks, and performance insights.

  • Support AI solution engineers by providing training datasets and defining model evaluation metrics.

• Visualization & Reporting:

  • Build interactive dashboards and reports using visualization tools to present insights to business stakeholders.

  • Translate complex data findings into actionable business recommendations.

Innovation Enablement

• Participate in the Innovation Track, supporting proof-of-concept projects that leverage AI and analytics.

• Conduct extensive literature reviews to determine the most effective multi-modal classification algorithms.

• Document methodologies and contribute to an internal data science playbook for repeatable value creation.

Risk and Compliance

• Ensure compliance with data governance and privacy frameworks.

• Build models for predicting risk to enable proactive mitigation.

• Provide insights into predicting future sales and reducing claims costs.

Customer

• Collaborate with software developers and data engineers to establish reliable data pipelines, ensuring data integrity, lineage, and governance.

• Foster strong relationships with internal and external stakeholders by providing data-driven solutions to their challenges.

• Participate in cross-functional projects to implement end-to-end solutions.

People

• Organize training sessions and workshops to share knowledge and best practices with the broader organization.

• Create a culture of ownership and accountability in support of the company's values.

• Education:

  • Master's degree in Data Science, Applied Mathematics, Statistics, or a closely related field (equivalent to NQF Level 9).

• Preferred:

  • Professional certification in Machine Learning, Artificial Intelligence, or Information Management.

  • Doctoral (PhD) degree in Data Science, Applied Mathematics, Statistics, or a related discipline (equivalent to NQF Level 10).

• Essential:

  • Minimum of 7+ years of experience in data science, analytics, or AI engineering.

  • Proven track record of developing, validating, and deploying predictive models into production environments.

  • Strong proficiency with SQL, APIs, and data libraries.

  • Excellent analytical and logical problem-solving ability.

  • Strong business acumen with the ability to clearly communicate complex insights to non-technical stakeholders.

  • A curiosity-driven and innovation-focused mindset.

  • Proven ability to collaborate effectively within cross-functional teams of developers and engineers.

• Preferred:

  • Exposure to Responsible AI frameworks and data governance standards.

  • Awareness of data privacy, fairness, and responsible AI principles.

  • A passion for continuous learning and the application of emerging technologies.