Senior Manager: Data Science
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About the Role
CredTech, a subsidiary of MTN Group FinTech, seeks a Senior Manager: Data Science to lead the design, development, and deployment of machine learning systems and credit risk prediction platforms.
Key Skills for This Role
Responsibilities
- 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
- Work at the interface of cloud, on premise, open source engineering, credit risk decisioning and emerging agentic / LLM enabled workflows
- Ensure execution quality, delivery discipline, model governance and capability uplift required to support CredTech's ambition to become Africa's leading digital lending platform
Requirements
- Minimum 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
Full Job Posting
Company Overview
- CredTech is a 100% owned subsidiary of MTN Group FinTech, aiming to become Africa's largest digital lending platform.
- CredTech delivers technology and credit scoring services, enhancing credit scoring, accelerating time to market, and improving debt and risk management.
Key Responsibilities
- Translate the CredTech and BankTech Data Science strategy into executable machine learning systems, prediction capabilities, and decisioning platforms.
- 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.
- Operate across Azure based tooling, including Azure AI Foundry and adjacent Azure ML capabilities, and partner environments that are Linux based and Python centric.
- Work at the interface of cloud, on premise, open source engineering, credit risk decisioning and emerging agentic / LLM enabled workflows.
- Ensure execution quality, delivery discipline, model governance and capability uplift.
Must Have Qualifications
- Minimum 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.
Preferred Qualifications
- MBA Degree.
- AI/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.
Additional Information
- Position Location: Dubai, United Arab Emirates.
- Application 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.
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