Senior Data Scientist: Big Data , AI

When it comes to putting people first, we’re number 1.

The number 1 Top Employer in South Africa.
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• 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…