Senior Data Scientist

About the Role

The number 1 Top Employer in South Africa. Certified by the Top Employer Institute 2026.

Responsibilities

• Vodacom is a leading African connectivity, digital and financial services company, providing voice, messaging, data, financial and converged digital services to over 130 million customers across multiple African markets. We are truly Customer Obsessed, passionate about exceeding customer expectations, working relentlessly to understand the customer, and viewing every decision through the customer’s eyes. Vodacom Group’s mission is to “connect people, enrich lives and empower society”, and Big Data, AI and Intelligent Automation sit at the heart of how we deliver on that promise.
• In South Africa, Vodacom has built a mature, deeply integrated Big Data, AI and Intelligent Automation (IA) operation embedded across multiple business areas. As a Senior Data Scientist, you will be a core builder within that operation, turning data and AI into measurable commercial value for Vodacom South Africa.
• The Senior Data Scientist is a hands-on technical specialist within the Vodacom South Africa Big Data team, responsible for owning and delivering machine learning and AI use cases end-to-end, from problem framing and data exploration through to model development, deployment and monitoring. The role works across Vodacom’s three strategic AI impact pillars (Customer Experience, Monetization and Productivity) to build solutions that create real impact for the business and its customers.
• Working under the direction of the Principal Data Scientist, the Senior Data Scientist combines strong applied modelling skill with sound engineering practice, contributing to reusable AI services, mentoring junior data scientists and graduates, and partnering closely with business and technology stakeholders. The ideal candidate is well grounded in Customer Value Management (CVM) principles and recommender systems, and is increasingly fluent in Generative and Agentic AI and modern MLOps practice.
• End-to-end use cases: own machine learning and AI use cases end-to-end, including framing the business problem, exploring and preparing data, engineering features, building and validating models, and supporting production deployment.
• Real-world ML products: develop machine learning and recommender solutions that solve real business problems, taking account of user needs, the technology landscape and operational constraints.
• CVM and personalisation: build churn, propensity, uplift and recommendation models that support Customer Value Management, hyper-personalisation and next-best-action across the VSA customer base.
• GenAI and agentic solutions: contribute to the design and build of Generative AI and agentic solutions, including LLM-based and RAG patterns, where they add measurable business value.
• Experimentation: design and run robust experiments and A/B tests, drawing meaningful conclusions from raw datasets and quantifying the impact of deployed models.
• Production-grade code: write clean, well-documented, reproducible code and implement data transformations and algorithms in Python and SQL.
• MLOps practice: apply MLOps practices such as version control, CI/CD, model monitoring and drift detection to ensure models are reliable, maintainable and observable in production.
• Reusable assets: contribute to the team’s shared AI services repository, building reusable components, pipelines and features that reduce time-to-value for future use cases.
• Governance and compliance: ensure all solutions adhere to data governance, responsible-AI and POPIA requirements, including privacy-by-design and appropriate model documentation.
• Insight translation: translate complex datasets into clear, actionable insight, communicating simply to non-technical audiences and visualising results to build understanding and buy-in.
• Stakeholder collaboration: partner with business, CVM, marketing and technology teams to understand requirements and translate business issues into analytics solutions.
• Delivery cadence: deliver against the quarterly PI (Programme Increment) planning cycle, managing priorities and dependencies to ship value-producing work reliably.
• Visualisation: produce dashboards and visualisations using tools such as Qlik Sense, Power BI or Tableau to surface model outputs and business impact.
• Mentorship: mentor and support junior data scientists and graduates, sharing knowledge on statistical techniques, algorithms, coding standards and ways of working.
• Community of practice: contribute to the team’s standards, code reviews and community of practice, helping raise the overall quality bar.
• Continuous learning: stay current with emerging tools, techniques and AI trends, bringing new ideas and approaches into the team.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics or a related quantitative field (essential).
• Honours or Master’s degree in a quantitative field will be advantageous.
• A minimum of 5 to 8 years of practical, hands-on experience in data science, machine learning or a closely related field.
• Demonstrated track record of building and deploying machine learning models that delivered measurable business impact in a commercial / consumer environment.
• Solid grounding in Customer Value Management principles, recommender systems and MLOps practice.
• Practical exposure to Generative AI, Large Language Models (LLMs) and / or agentic AI is highly advantageous.
• Experience working with large volumes of structured and unstructured data.
• Experience mentoring or guiding more junior team members is advantageous.
• Experience working in cloud environments (AWS, GCP and / or Azure) is advantageous.
• Strong proficiency in Python and its data science and ML stack (e.g. pandas, NumPy, scikit-learn, SciPy, NLTK).
• Hands-on experience with deep learning and gradient-boosting frameworks such as PyTorch, TensorFlow, XGBoost or H2O.
• Strong SQL skills and the ability to work with large-scale data using tools such as PySpark, NoSQL and streaming frameworks (Kafka, Flink).
• Working knowledge of MLOps tooling such as MLflow, Kubeflow or SageMaker, and version control with Git.
• Familiarity with containerisation (Docker) and an awareness of orchestration (Kubernetes).
• Exposure to LLM patterns such as prompt engineering and Retrieval-Augmented Generation (RAG) is advantageous.
• Proficiency with visualisation tools such as Qlik Sense, Tableau, Power BI or Plotly.
• Familiarity with cloud-native data and AI services (e.g. AWS SageMaker, Glue, Athena; GCP; or Azure ML) is advantageous.

Requirements

• Significant experience in machine learning and the deployment of models and algorithms from large volumes of structured and / or unstructured data.
• Ability to test hypotheses from raw datasets, draw meaningful conclusions and communicate results verbally, in writing and through effective visualisation.
• Strong problem-solving skills and a delivery mindset, with the ability to manage priorities and ship reliably.
• Ability to work across cross-functional teams to translate business issues into analytics solutions.
• Strong communication skills, including prior experience presenting analytics results to senior stakeholders.
• A collaborative, customer-obsessed approach with a commitment to responsible and ethical AI.
• Curiosity and a commitment to continuous learning in a fast-changing AI landscape.
• Enticing incentive programs and competitive benefit packages
• Retirement funds, risk benefits, and medical aid benefits
• Cell phone and data benefits, advantages fibre connection discounts, and exclusive staff discounts offered in collaboration with partner companies