Senior Manager: Data Science

Senior Manager: Data Science – CredTechCredTech is a 100% owned subsidiary of MTN Group FinTech. CredTech aims to emerge as Africa’s largest digital lending platform with operational and commercial excellence serving as its foundational pillars for achieving unprecedented success. CredTech strives to deliver technology and credit scoring services by establishing comprehensive tech and product capabilities, enhancing credit scoring, accelerating time to market, and improving debt and risk management through strategic partnerships.We at CredTech believe this is a game changer in terms of our business strategy. We are looking at an incumbent to join us as we build a successful business together.As part of your portfolio as Senior Manager: Data Science – CredTech, you will be responsible to.:Translate the CredTech and BankTech Data Science strategy into executable machine learning systems, prediction capabilities, and decisioning platforms across CredTech markets.Lead the design, development, deployment and operationalisation of machine learning, MLOps, credit risk prediction systems, optimisation models, and AI-enabled tooling.Provide technical and people leadership across a team of machine learning managers, engineers and data scientists, ensuring that analytical models are production-ready, scalable, governed and aligned to commercial, risk and operational outcomes.Operate across Azure-based tooling, including Azure AI Foundry and adjacent Azure ML capabilities, while also recognising partner technology environments that are Linux-based and Python-centric. The incumbent will work at the interface of cloud, on-premise, open-source engineering, credit risk decisioning and emerging agentic / LLM-enabled workflows.Execution quality, delivery discipline, model governance and the capability uplift required to support CredTech's ambition to become Africa's leading digital lending platform.The incumbent must have the following:Minimum of 4-year tertiary degree in Computer Science, Mathematics, Statistics, Data Science, Engineering, Financial, Commerce, Statistical or related field.Master's Degree in Computer Science, Data Science, Statistics, Financial, Commerce, Engineering or related field.Minimum 8 to 12 years of relevant work experience in machine learning, data science, decision science, MLOps, analytics engineering or related technical fields.Minimum 3 to 5 years' experience in a senior technical leadership or people management role.Experience leading and managing production machine learning models in a highly data-driven environment, preferably within fintech, banking, consumer finance, telecommunications, digital lending or financial services.Experience with credit risk prediction systems, behavioural models, affordability models, limit strategies, decisioning systems or portfolio optimisation.Hands-on experience with Python, SQL, Linux-based development environments and modern data science / machine learning libraries.Experience with MLOps tooling such as MLflow, Kubeflow, Azure ML, model registries, experiment tracking and monitoring frameworks.Experience with APIs, microservices, containerisation, CI/CD, Git-based workflows, Docker and Kubernetes or equivalent orchestration technologies.English speaker; French, Chinese and African languages advantageous.What will give an edge to your application:MBA DegreeAI/ML, cloud, MLOps, Azure or data science professional certification.Experience working with Azure-based AI/ML services, including Azure ML and/or Azure AI Foundry.Working knowledge of Hadoop, Spark, PySpark or other distributed computing frameworks.Exposure to reinforcement learning, optimisation, simulation or policy learning for commercial / credit risk use cases.Exposure to LLM-based tooling, agentic workflows, prompt engineering, retrieval augmented generation or AI-assisted development workflows.Experience working across multiple markets, cultures, vendors, partners and technology environments.Position Location: Dubai, United Arab EmiratesApplication Closing date: 22 June 2026. Late applications will not be accepted.Should you not hear from us within 2 weeks of closing date, please consider your application unsuccessful.