AI Research Scientist (Human Capital)

KEY FOCUS AREAS 

• Applied AI Research, Experimentation, and Innovation 

• Strategic Opportunity Shaping and Portfolio Contribution 

• Data Readiness, Quality, and Engineering Partnership 

• Advanced Analytics, Insight Generation, and Solution Refinement 

• Responsible AI, Risk, and Regulatory Alignment 

• Stakeholder Engagement, Communication, and Influence 

• Capability Building, Knowledge Sharing, and Continuous Learning 

KEY ACCOUNTABILITIES 

Applied AI Research, Experimentation, and Innovation 

• Conduct cutting-edge applied research to develop innovative AI algorithms and models that address specific business challenges and enhance analytics capabilities. 

• Design and run rigorous experiments to compare model approaches, validate hypotheses, and quantify impact using appropriate statistical and machine learning evaluation methods. 

• Produce research artefacts such as technical designs, prototypes, reproducible code, and evidence packs that support decision-making and downstream implementation. 

Strategic Opportunity Shaping and Portfolio Contribution 

• Collaborate closely with the Manager: New Tech and AI to identify strategic opportunities for AI implementation that align with organisational goals and measurable outcomes. 

• Define AI use-case value propositions, success metrics, and feasibility considerations to support prioritisation across multiple markets and business units. 

• Provide expert input into AI roadmaps by recommending emerging methods, tools, and partnership options that increase speed-to-value and long-term sustainability. 

Data Readiness, Quality, and Engineering Partnership 

• Work alongside data engineers to ensure the availability, traceability, and quality of data necessary for training, validating, and monitoring AI models. 

• Specify data requirements, feature needs, and labelling strategies that reduce bias, improve model performance, and support reproducibility across environments. 

• Partner with data governance stakeholders to ensure datasets and pipelines meet security, privacy, retention, and cross-border data handling requirements. 

Advanced Analytics, Insight Generation, and Solution Refinement 

• Analyse complex datasets to extract actionable insights that inform the development, tuning, and refinement of AI solutions. 

• Identify drivers of performance, drift, bias, and failure modes and recommend mitigation strategies that improve robustness in real-world conditions. 

• Collaborate with domain specialists to convert analytical insights into deployable decision logic, process improvements, and product enhancements. 

Responsible AI, Risk, and Regulatory Alignment 

• Contribute to the development and adoption of best practices for responsible AI, data management, and AI model utilisation, ensuring compliance with governance standards. 

• Implement model documentation, explainability approaches, and testing controls that support auditability, transparency, and model risk management requirements. 

• Ensure AI solutions reflect fairness, inclusion, and appropriate use principles across diverse pan African customer and employee populations. 

Stakeholder Engagement, Communication, and Influence 

• Present research findings and technical concepts to both technical and non-technical stakeholders, ensuring clarity, engagement, and decision readiness. 

• Translate complex AI concepts into business language for Group CoEs, HC Services, and HC Business Partnering teams to support adoption and change enablement. 

• Facilitate alignment on requirements, deliverables, and ethical considerations across stakeholders, ensuring shared understanding and accountability. 

Capability Building, Knowledge Sharing, and Continuous Learning 

• Stay abreast of the latest advancements in AI and machine learning and apply relevant new methodologies to continuously improve the effectiveness of AI initiatives. 

• Contribute to internal communities of practice through playbooks, reference architectures, training sessions, and coaching that uplift AI literacy and safe adoption. 

• Support talent development by mentoring junior practitioners and promoting strong engineering and scientific standards across the AI delivery ecosystem. 

KNOWLEDGE & SKILLS 

Knowledge 

• Applied machine learning and modern AI methods (including deep learning and generative AI patterns).  

• Responsible AI, model risk management, and governance in regulated financial services.  

• Data engineering concepts, data quality management, and analytics platforms.  

• Experiment design, statistical evaluation, and measurement of model/business impact.  

• Product and platform delivery concepts, including productionisation and monitoring.  

• Pan African regulatory and operational considerations impacting data and AI deployment. 

Skills 

• Strong research and problem-framing capability to convert business needs into testable AI approaches.  

• Advanced data analysis and feature engineering capability to improve model performance and robustness.  

• Clear technical communication skills that enable stakeholder decision-making and adoption.  

• Practical solution design skills that bridge prototype-to-production considerations.  

• Influence and collaboration skills across cross-functional teams in a matrix environment.  

• Continuous learning agility with the ability to evaluate and adopt emerging AI techniques responsibly. 

QUALIFICATIONS & EXPERIENCE  

Education / Qualification 

• Bachelor’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or related fields. 

• Master’s degree or PhD in a relevant discipline is advantageous, particularly for research-intensive focus areas. 

• Relevant certifications (advantageous): cloud ML engineering, data governance, responsible AI, or analytics engineering. 

Work Experience 

• 3 - 5 years' experience, including a track record of 2 years in technical position.   

• Exposure to client service and quality management is preferred.  

COMPETENCIES 

Technical Competencies 

• Python and common ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and reproducible experimentation practices.  

• Modern AI approaches (e.g., representation learning, sequence modelling, generative AI patterns, retrieval-augmented techniques, evaluation frameworks).  

• Data querying and processing (e.g., SQL, distributed compute concepts) and feature pipeline collaboration.  

• Model evaluation, explainability, robustness testing, bias/fairness assessment, and performance monitoring.  

• MLOps concepts such as CI/CD for ML, model registries, model monitoring, drift detection, and controlled retraining.  

• Secure AI delivery practices aligned to enterprise security and privacy requirements.