Senior Manager Modelling & AI

Requisition Details & Talent Acquisition Specialist

REQ 142099: Keabetswe Modise

Closing Date: 05 December 2025

Job Family

Career Stream

IT Application Development

Leadership Pipeline

Job Purpose

To lead and grow a high-performing team focused on advanced Machine Learning (ML) modelling and artificial intelligence capabilities that drive strategic value across the organization. This role is accountable for the development and operationalization of cutting-edge AI solutions including predictive modelling and generative AI. It enables scalable reusable and ethical AI practices by fostering cross-functional collaboration embedding robust governance and aligning with enterprise-wide data and digital strategies.

Job Responsibilities

• Define grow and leada team of Data Scientists and ML Modelling Experts across proficiency levels fostering technical excellence delivery discipline and innovation.
• Develop and deploy traditional ML modelsacross key financial services domains fraud detection collections AML operations etc; using techniques like regression classification clustering and time-series analysis to support decision-making and regulatory compliance.
• Advance customer and business intelligencethrough behavioural modelling segmentation lifetime value prediction churn modelling and recommendation systems leveraging ensemble methods graph-based learning and temporal feature engineering to drive personalization and strategic growth.
• Explore and integrate advanced modelling approaches including deep learning graph neural networks and retrieval-augmented generation (RAG) models to enhance model performance enable contextual understanding from unstructured data and support emerging use cases such as document intelligence and GenAI-assisted analytics.
• Apply specialised ML techniquessuch as computer vision natural language processing (NLP) and large language models (LLMs) to solve domain-specific challenges including document classification KYC automation sentiment analysis and intelligent customer interaction across banking channels.
• Identify opportunities across the Nedbank Groupto enhance model performance and scalability through foundational capabilities such as feature engineering graph-based data representation and reusable modelling assets.
• Apply financial services domain knowledgeto ensure models are aligned with regulatory requirements business priorities and industry-specific data characteristics.
• Collaborate closely with internal stakeholders including business data science engineering and platform teams to ensure modelling solutions are integrated governed and strategically aligned.
• Introduce and support GenAI capabilities particularly retrieval-augmented generation (RAG) models where they complement traditional modelling e.g. enhancing model explainability document summarization or contextual data retrieval.
• Design and manage data pipelinesthat support both traditional ML and GenAI workflows including real-time and batch feature computation from structured and unstructured data sources.
• Drive innovation in feature creation leveraging advanced techniques such as graph-based feature extraction temporal feature engineering and embedding generation.
• Lead the implementation and operation of scalable reliable and governed modelling platforms ensuring they are production-ready secure and aligned with business needs.
• Own the lifecycle of modelling assets including availability documentation versioning monitoring and governance to ensure high-quality trusted inputs for ML and AI solutions.
• Solve complex unstructured problemswith a detail-oriented mindset working independently and driving initiatives to completion.
• Possess strong business and communication skills enabling effective collaboration with business owners to define key modelling needs and ensure foundational assets meet those needs.

Job Responsibilities Continue

• Manage financial and business results ensuring delivery within budget and timelines and compliance with divisional billing and cost recovery processes.
• Deliver high-quality modelling systems and processesaligned to Nedbanks strategic goals data strategy and AI roadmap.
• Provide timely professional advice and strategic inputto stakeholders ensuring delivery within agreed quality budget and time parameters.
• Build and maintain strong stakeholder relationshipsby delivering consistent high-value modelling services and solutions.
• Actively engage with clients partners and internal teamsto build trust align expectations and ensure delivery of best-practice modelling foundations.
• Promote knowledge sharing and collaborationacross teams and departments to strengthen the modelling and AI capability.
• Operationalize divisional strategyby aligning team priorities and empowering first-line managers with clear roles performance measures and delivery goals.
• Leverage professional frameworks tools and technologiesto deliver scalable strategic modelling solutions.
• Manage multiple foundational modelling assetsthrough strategic planning implementation and continuous improvement.

Essential Qualifications - NQF Level

Preferred Qualification

• Tertiary Qualification/ formal accreditation in STEM related field
• BSC Computer Science BSc Engineering Econometrics Mathematical Statistics Actuary Science.
• Masters or Doctorate will be an added advantage.
• Post graduate management qualification/MBA

Essential Certifications

Minimum Experience Level

• Minimum 6 to 8 years Data Science experience with 1-2 years management experience

Technical / Professional Knowledge

• Deep understanding of Machine Learning Statistics Optimization or related fields with a strong emphasis on feature engineering data representation and model architecture design tailored to financial services use cases.
• Proficiency in Python (required) with experience in additional languages such as R Scala or Java being advantageous for integrating with enterprise systems and legacy platforms.
• Demonstrated experience applying machine learning foundations within the financial services sector with a strong understanding of domain-specific data regulatory considerations and business drivers across risk fraud customer intelligence and operational modelling.
• Experience working with large-scale datasets and distributed computing tools(e.g. Spark Ray) particularly for feature computation transformation and scalable model training.
• Proven track record in delivering end-to-end ML use cases with a focus on foundational components like feature stores graph-based data structures and reusable modelling assets.
• Hands-on experience with GenAI and retrieval-augmented generation (RAG) models including the use of vector databases embedding models and prompt engineering to support document intelligence contextual search and hybrid ML-AI workflows.
• Ability to translate complex data concepts into business-relevant narratives and insights enabling strategic decision-making and stakeholder alignment.
• Excellent written and verbal communication skills with a strong ability to collaborate across cross-functional teams including data engineering business and platform stakeholders.
• Experience in budgeting business administration and strategic planning with a focus on aligning modelling initiatives to divisional and enterprise goals.
• Knowledge of change management and client service management principles ensuring smooth adoption and integration of modelling solutions.
• Familiarity with governance risk and controls especially in the context of data and ML asset management model risk and regulatory compliance.
• Strong stakeholder management and influencing skills with the ability to navigate complex organizational structures and drive consensus.
• Experience in employee development talent management and workforce planning fostering a high-performance modelling team.
• Understanding of project management principles and relevant regulatory frameworks including POPIA Basel and IFRS where applicable.
• Skilled in business writing management reporting and communication strategies supporting executive-level engagement and reporting.
• Familiarity with the System Development Life Cycle (SDLC) ITIL and IT architecture ensuring modelling solutions are aligned with enterprise technology standards.
• Experience with graph databases (e.g. Neo4j TigerGraph)and graph analytics particularly for feature engineering and relationship modelling in financial datasets.
• Understanding of IT asset management processes and joint application development practices supporting scalable and governed modelling infrastructure.
• Ability to work within and influence complex organizational structures driving strategic modelling initiatives across multiple squads and domains.

Behavioural Competencies