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Senior Manager : Modelling & AI
NedbankJohannesburg, ZA3 days ago
Job descriptionTo 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. Define, grow, and lead a team of Data Scientists and ML Modelling Experts across proficiency levels, fostering technical excellence, delivery discipline, and innovation. Develop and deploy traditional ML models across 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 intelligence through 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 techniques such 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 Group to enhance model performance and scalability through foundational capabilities such as feature engineering, graph-based data representation, and reusable modelling assets. Apply financial services domain knowledge to 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 pipelines that 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 problems with 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. 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 processes aligned to Nedbank’s strategic goals, data strategy, and AI roadmap. Provide timely, professional advice and strategic input to stakeholders, ensuring delivery within agreed quality, budget, and time parameters. Build and maintain strong stakeholder relationships by delivering consistent, high-value modelling services and solutions. Actively engage with clients, partners, and internal teams to build trust, align expectations, and ensure delivery of best-practice modelling foundations. Promote knowledge sharing and collaboration across teams and departments to strengthen the modelling and AI capability. Operationalize divisional strategy by aligning team priorities and empowering first-line managers with clear roles, performance measures, and delivery goals. Leverage professional frameworks, tools, and technologies to deliver scalable, strategic modelling solutions. Manage multiple foundational modelling assets through strategic planning, implementation, and continuous improvement. Advanced Diplomas / National 1st Degrees 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 ITIL Talent nurturing or equivalent MMP / SMP / MM or equivalent Minimum 6 to 8 years Data Science experience with 1-2 years management experience 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. Building Partnerships Facilitating Change Inspiring others Business Acumen Building partnerships Driving for Results Selecting Talent Research / Data Analysis jobs
Job title : Senior Manager : Modelling & AI
Job Location : Gauteng, Johannesburg
Deadline : October 24, 2025
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Senior Manager • Johannesburg, ZA
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