Manager - ML

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 Manager: BI Reporting must operate across diverse markets, teams, and data sources, ensuring the delivery of consistent, trusted, and automated reporting outputs. Helping productise insights for internal and external markets across Africa's growing digital data economy. 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. 
Our values dubbed, LIVE Y'ello, are the cornerstone of our culture.

Lead with Care
Can-do with Integrity
Collaborate with Agility
Serve with Respect
Act with Inclusion

ML Engineering & Pipeline Development

•    Build and optimise end-to-end machine learning pipelines (feature extraction, training, scoring, monitoring).

•    Convert Data Scientist prototypes into production-grade model code and scalable pipelines.

•    Implement CI/CD for ML using tools such as MLflow, Kubeflow, Airflow, Azure ML, or GCP Vertex AI.

2.    MLOps, Deployment & Automation

•    Deploy ML models across cloud, hybrid, or on-prem environments based on DataCo standards.

•    Automate model retraining, versioning, rollout, rollback, and dependency management.

•    Ensure reproducibility, auditability, and governance of AI pipeline

3.    Model Monitoring, Observability & Governance

•    Implement model monitoring dashboards tracking drift, latency, accuracy, and fairness.

•    Build alerting mechanisms for model degradation or operational failure

•    Maintain compliance with MTN Responsible AI, privacy and security standards.

4.    Data Engineering & Integration Support

•    Collaborate closely with Data Engineering teams to ensure model-ready data pipelines.

•    Build feature stores, data transformation pipelines, or reusable data components supporting ML workloads.

•    Optimise data ingestion workflows for high-volume telco, geospatial and behavioural datasets.

5.    Cross-functional Collaboration & Delivery

•    Work within agile squads alongside Data Scientists, Product Owners, Engineers, and Delivery teams.

•    Participate in sprint ceremonies and contribute to backlog refinement.

•    Translate technical designs into scalable solutions for internal users and external clients.

6.    Documentation & Enablement

•    Produce technical documentation covering ML architectures, APIs, pipelines, monitoring, and deployment logic.

•    Provide technical onboarding and support to OpCo teams adopting DataCo ML solutions.

•    Contribute to AI/ML best-practice frameworks, toolkits, and internal knowledge bases.

Education:

•    3 year Bachelor's degree in Computer Science, Software Engineering, Data Engineering, AI/ML, or related field

•    Postgraduate qualification preferred

•    Certifications in cloud platforms (Azure, GCP, AWS), MLOps, or ML engineering advantageous

Experience:

•    4–6 years' experience in ML engineering, MLOps, or machine learning deployment

•    Experience building ML pipelines using cloud-native tools (Azure ML, GCP Vertex AI, Databricks, MLflow, Kubeflow)

•    Demonstrated experience deploying ML models to production in enterprise environments

•    Strong experience in Python, Spark, CI/CD (GitHub Actions, Jenkins), containerisation (Docker, Kubernetes)

•    Exposure to telco, geospatial, behavioural or large-scale datasets advantageous

•    Prior experience in cross-functional, agile delivery squads

Competencies:

•    Strong ML engineering capability (scalable pipelines, APIs, cloud ML)

•    Proficiency in Python, PySpark, SQL, Big Data frameworks

•    Deep knowledge of CI/CD, DevOps and MLOps tooling

•    Experience with monitoring, drift detection, logging, and observability

•    Understanding of Responsible AI practices and compliance frameworks

•    Problem Solver & Innovative Thinker

•    Results-Driven & Operationally Astute

•    Cross-Functional Collaborator

•    Strong Communicator (technical & non-technical)

•    Agile mindset, adaptable to evolving environments

Key Deliverables
Internal Deliverables

•    Production-ready ML pipelines with CI/CD integration

•    Automated monitoring dashboards and model performance logs

•    Feature stores and reusable pipeline modules

•    Deployment playbooks, runbooks, and engineering documentation

•    Scalable inference environments powering Group and OpCo use cases

External Deliverables

•    ML deployment assets used in DataCo consulting engagements

•    API-based ML services for data monetisation products

•    Technical documentation and integration guides for clients

•    Model hosting & monitoring frameworks enabling client applications

Skills

•    Strong proficiency in BI tooling (Power BI/Tableau), SQL, data modelling.

•    Understanding of data engineering pipelines and cloud data environments.

•    Familiarity with AI/ML concepts and analytics productisation beneficial.

•    Data governance, metadata, and KPI standardisation experience.