Senior Manager - Data Scientist.Group Strategy and Transformation

Global Influences:

The global economy is undergoing rapid digital transformation, with AI and machine learning emerging as core differentiators for competitive advantage. Enterprises are embedding predictive intelligence into their operations, products, and customer experiences to enhance efficiency, agility, and revenue growth. Meanwhile, cloud-native analytics platforms, federated learning, and MLOps frameworks are standardising how AI is scaled across complex, multi-market organisations.
Heightened focus on data ethics, fairness, and regulatory compliance is also shaping the design of AI systems. As governments tighten rules around data privacy, bias mitigation, and model explainability, organisations must ensure that AI is not only effective but also responsible and trustworthy.

Industry Drivers:
In the telecommunications sector, predictive analytics has become central to driving value from large-scale, real-time data generated by mobile networks, customer usage, and behavioral patterns. Use cases such as churn prediction, next-best-offer recommendations, network anomaly detection, and location-based targeting are no longer aspirational , they are expected. As telcos evolve into techcos and platform businesses, the ability to productise and monetise AI capabilities across industries such as FMCG, Retail, finance, and public services becomes a critical growth lever.
Additionally, as data monetisation becomes mainstream, clients are not just asking for raw data but for intelligent insights, predictive signals, and decision-ready outputs  all of which require robust data science capacity.

Organisational Mandate:

MTN DataCo is positioned at the forefront of MTN Group’s transformation into a data-powered, AI-native enterprise. The organisation is building a multi-OpCo data ecosystem capable of scaling advanced analytics across internal functions (e.g., marketing, sales, finance, networks) and external markets. This includes delivering reusable AI models, creating monetisable analytics solutions, and integrating intelligence into products and consulting services.

The Senior Manager: Data Scientist plays a mission-critical role in this vision. By designing and deploying scalable, ethical, and production-grade machine learning models, the role helps embed intelligence into MTN’s operational DNA. The role also supports the consulting and monetisation agendas by co-creating sector-relevant AI solutions for enterprise clients, enabling MTN to lead in Africa’s growing $2B+ data economy.
Organization Values: At MTN we believe that understanding our people’s needs and aspirations is key to creating experiences that delight you at work, every day. We are committed to fostering an environment where every member of our Y’ello Family is heard, understood, and empowered to live an inspired life. Our values keep us grounded and moving in the right direction. Most importantly, they keep us honest. It is not something we claim to be. It is in our DNA.
As an organisation, we consider it our mission to create an exciting and rewarding place to work, where our people can be themselves, thrive in positivity and ignite their full potential. A workplace that boosts creativity and innovation, improves productivity, and ultimately drives meaningful results. A workplace that is built on relationships and achieving a purpose that is bigger than us. This is what we want you to experience with us!
Our commitments go beyond an organisational promise. It is in our leadership and managerial ethos to meaningfully partner with our employees, customers, and stakeholders with a vision to realise our shared goals. 
 

Model Design & Development:

•     Lead the development of advanced machine learning models and AI frameworks that leverage telco, geolocation, and behavioural signals.
•     Define modelling standards and promote reuse of pipelines, features, and architectures across multiple use cases.

2.    Ai Solution Packaging:

• Architect AI-enabled components that can be operationalised as scalable products or consulting accelerators across OpCos.•    Guide the modularisation of ML solutions for deployment in different sectors (e.g. financial services, retail, government).3.    Use Case Leadership & Translation:
• Engage with executive and business stakeholders to identify high-impact use cases aligned to commercial and operational KPIs.•    Define the success criteria, data requirements, and validation frameworks that underpin each AI initiative.

4.    Mlops And Deployment:

•     Partner with Engineering and DevOps to ensure seamless model deployment into production environments using modern CI/CD, containerisation, and MLflow/Kubeflow pipelines.
•     Embed traceability, auditability, and scalability as core principles of ML deployment practices.

5.    Model Monitoring & Ethics:

•     Lead efforts to track model health, bias, drift, and fairness in accordance with Responsible AI and ethical governance frameworks.
•     Define audit protocols and escalation paths for AI risk mitigation and regulatory compliance.

6.    Cross-Functional Collaboration:

•     Serve as the AI representative within multidisciplinary squads, aligning workstreams across Data Engineering, Product, Governance, and Commercial teams.
•     Translate technical complexity into strategic narratives that influence senior leadership and client stakeholders.

7.    Leadership & Innovation Expectations

•    Champion a data science culture of experimentation, intellectual curiosity, and measurable business value.
•    Drive innovation in AI use cases, including real-time intelligence, geospatial modelling, and deep learning frameworks.
•    Build communities of practice for AI excellence across MTN OpCos.
•    Represent MTN DataCo in regional and global forums to promote leadership in responsible and impactful AI applications
 

. Job Requirements (Education, Experience and Competencies)

Education:

•     4 year degree in Computer Science, Statistics, Applied Mathematics, Data Science, or a related quantitative field (required)
•     Master’s degree or PhD in Machine Learning, Artificial Intelligence, or a relevant discipline (preferred)
•     Certifications in machine learning, cloud AI services (e.g. GCP ML, Azure AI, Databricks), or Responsible AI frameworks (advantageous)

Experience:

•     Minimum of 5 - 8 years of progressive experience in data science, AI/ML solution development, or advanced analytics, with a proven record of production-grade deployments
•     At least 3+ years in a senior leadership or team lead role guiding AI initiatives in enterprise settings
•     Demonstrated success in developing and scaling AI/ML solutions using telco, location, or behavioural datasets
•     Strong experience working in cloud-based MLOps environments (e.g. Azure ML, GCP Vertex AI, Databricks, MLflow/Kubeflow)
•     Exposure to cross-sector AI deployments, including financial services, retail, or public sector use cases, is highly advantageous
•     Proven track record of working in agile, cross-functional teams, ideally in platform, product, or consulting environments
•     Prior experience in telco, urban planning, consulting, or retail analytics domains
•     Demonstrated experience with big data datasets in customer behaviour, geospatial, etc.
•     Proven ability to deploy models into production and monitor at scale

Competencies:

•     ML & AI Expertise: Deep understanding of supervised/unsupervised learning, time series, recommendation systems, classification, NLP, and spatial ML
•     Solution Packaging: Ability to abstract models into reusable components, APIs, or solution accelerators
•     MLOps Proficiency: Practical experience with model versioning, testing, orchestration, and monitoring in production
•     Responsible AI & Ethics: Working knowledge of fairness, bias detection, explainability, and AI risk governance
•     Strategic Translation: Strong business acumen with ability to align technical work to commercial and operational outcomes
•     Technical Leadership: Capable of mentoring junior data scientists and leading architectural discussions
•     Communication & Influence: Clear communicator who can engage C-level audiences and simplify technical concepts for non-technical stakeholders

Key Deliverables:
•    Internal:
o    Enterprise-grade ML Models: Deployed predictive and prescriptive models that directly inform business-critical decisioning across Group and OpCos (e.g., churn prediction, product affinity, fraud detection).
o    Reusable AI Pipelines: Production-ready ML pipelines and feature stores, complete with automated retraining, performance monitoring, and MLOps documentation to support cross-functional reuse and scalability.
o    Strategic AI Use Case Portfolio: Curated catalogue of prioritised, high-impact AI use cases mapped to business KPIs, with supporting artefacts (problem statements, data assets, success metrics, and outcome reports).
o    Responsible AI & Governance Artefacts: Integrated bias assessment reports, fairness validations, and model documentation aligned to MTN’s AI governance standards.

•    External:
o    Client-ready AI Assets: Sector-specific AI solutions (e.g., footfall prediction, customer lifetime value, geospatial targeting) embedded in MTN’s PoV and consulting engagements.
o    Commercialised AI Offerings: Packaged machine learning modules and APIs contributing to MTN DataCo’s product catalogue and monetisation platforms.
o    Thought Leadership Contributions: Insight briefs, client presentations, and published use cases that position MTN as a pan-African leader in responsible and applied AI.
o    Partner Integration Support: Technical documentation and onboarding artefacts enabling secure and scalable integration of AI components into client or partner environments.