Senior Specialist: Data Science at Absa South Africa

Job Summary

• We are seeking a highly skilled and motivated Data Scientist / AI Engineer to join our growing Advanced Analytics and AI team within the Group Chief Analytics Office.
• Our organization is a leading financial services institution offering universal banking products and services across Africa. In this fast-paced, high-impact environment, the successful candidate will play a key role in the development, deployment, and operationalization of machine learning (ML), artificial intelligence (AI), generative AI, and agentic AI models.
• This role requires a strong technical foundation and the ability to apply data science and AI techniques within the regulatory, architectural, and risk frameworks of the financial services industry. You will work across functional teams to deliver intelligent solutions that drive innovation and business performance, while ensuring adherence to enterprise data and AI architecture standards and best practices.
• You will also mentor junior data scientists, guiding them in model selection, architecture design, and deployment strategies tailored to financial use cases such as credit risk, fraud detection, customer engagement, and personalization.

Job Description

Key Responsibilities:

• Design, develop, test, and deploy production-grade ML, AI, generative AI, and agentic AI models.
• Design, develop, test and deploy AI blueprints for reuse/ re-application across multiple AI use cases.
• Develop guardrails in accordance with group security and architecture standards.
• Partner with stakeholders across Business Units and Functions, e.g. Risk, Compliance, to identify, design, develop, deploy and manage impactful solutions.
• Translate complex business problems into structured tasks and deploy models that deliver measurable value.
• Ensure all solutions adhere to enterprise Data and AI architecture, security, governance, and AI/ML operationalization standards.
• Build model pipelines using enterprise MLOps frameworks, ensuring auditability, scalability, and performance in production.
• Monitor and maintain model performance, re-training and optimizing as needed in dynamic banking environments.
• Provide technical guidance and mentoring to data scientists across the Group, especially around model selection, experimentation protocols, and deployment best practices.
• Stay up to date with advancements in the field, including foundation models, multi-agent systems, and AI governance frameworks.

Required Skills:

• Proficiency in Python and data science toolkits (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch).
• Demonstrated experience designing and implementing ML and AI use cases using leading platforms such as Databricks, AWS Bedrock, or Amazon SageMaker.
• Hands-on experience with vector databases (e.g., Open search, Pinecone, FAISS, Milvus) for efficient similarity search and retrieval.
• Exposure to AI orchestration frameworks like LangChain, LlamaIndex Weaviate pipelines to build scalable, retrieval-augmented applications.
• Exposure to Model Evaluation framework to check ML and AI use cases accuracy and performance testing.
• Deep understanding of classical ML as well as generative AI (e.g., LLMs, GANs, VAEs) and agentic AI (e.g. autonomous agents, multi-agent coordination).
• Strong grasp of MLOps tools (e.g., MLflow, Kubeflow, Docker, Airflow, CI/CD) and cloud platforms, services and models.
• Experience deploying models in highly regulated environments, with strong attention to model risk, explainability, and compliance.
• Solid foundation in enterprise-grade data pipelines, governance, and architecture principles.
• Excellent interpersonal and communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Proven ability to work under pressure and manage competing priorities in a fast-moving, business-critical environment.

Experience:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field (PhD a plus).
• 3–5+ years of experience in applied data science or AI roles, preferably within financial services or banking.
• Demonstrated experience in designing and deploying AI/ML solutions into production, including generative AI.
• Familiarity with financial industry use cases such as credit scoring, fraud detection, KYC, personalization, and regulatory compliance analytics.
• Experience working within enterprise architecture and governance frameworks.
• Prior experience collaborating with, mentoring or coaching data scientists.

Education

• Bachelor`s Degrees and Advanced Diplomas: Statistics, Bachelor Honours Degree: Physical, Mathematical, Computer and Life Sciences

End Date: July 7, 2025