Role purpose:

The Big Data & Special CVM Projects Specialist is responsible for leveraging advanced analytics, machine learning, and customer intelligence to drive sustainable customer and revenue growth across the M Pesa business. The role develops and operationalizes data-driven CVM strategies that improve customer acquisition, engagement, retention, product penetration, and customer lifetime value.

The role is accountable for:

• Co-creating and deploying advanced machine learning and artificial intelligence solutions that drive revenue growth, cost optimization, customer retention, and enhanced customer experience.
• Developing big data monetization propositions and managing their end-to-end execution to unlock commercial value from customer data and behavioral insights.
• Delivering actionable customer insights, predictive analytics, and segmentation strategies that support M-Pesa objectives, commercial targets, and business decision-making.
• Designing and optimizing personalized customer lifecycle campaigns, including acquisition, activation, retention, reactivation, cross-sell, and upsell initiatives.
• Driving customer value enhancement through time to action, causal inference, difference in difference propensity modelling, churn prediction and customer journey optimization initiatives.
• Measuring and improving campaign effectiveness through Causal Inferences, advanced analytics, experimentation, and continuous optimization of customer engagement strategies.
• Promoting effective data governance, data quality, and responsible use of customer information while ensuring compliance with regulatory and privacy requirements.

Key accountabilities and decision ownership:

• Causal Inference, Transformer Based Modelling, Classical ML Modelling its Lifecycle.
• ETL, Feature Engineering Alternatives & Embedding Infrastructure – AI Product Lifecycle Strategy
• Explainable AI (XAI) & Model Governance
• E2E go to market execution for big data and special projects propositions.
• Identify Analyze and Interpret trends or patterns in complex data sets.

Core competencies, knowledge and experience:

• Excellent analytical, logical and casual reasoning skills.
• Strong stakeholder management skills
• Ability to anticipate customer, competitor and market dynamics
• Able to challenge the status quo

Must have technical/professional qualifications:

• Bachelor’s Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Actuarial Science, Artificial Intelligence, or a related quantitative discipline.
• Master’s Degree in Data Science, Analytics, Artificial Intelligence, Business Analytics, or MBA (preferred).
• Minimum 5–7 years’ experience in Big Data Analytics, Data Science, Customer Analytics, CVM, Machine Learning, or Commercial Analytics.
• Advanced proficiency in SQL and data querying tools.
• Experience using Python, Spark, Databricks, or equivalent analytics tools.
• Experience working with Big Data platforms, cloud analytics environments, and machine learning frameworks.
• Strong understanding of Fintech ecosystems.
• Experience developing and deploying predictive models in a production environment.

Who we are

We are a leading international Telco, serving millions of customers. At Vodafone, we believe that connectivity is a force for good. If we use it for the things that really matter, it can improve people’s lives and the world around us. Through our technology we empower people, connecting everyone regardless of who they are or where they live and we protect the planet, whilst helping our customers do the same.

Belonging at Vodafone isn’t a concept; it’s lived, breathed, and cultivated through everything we do. You’ll be part of a global and diverse community, with many different minds, abilities, backgrounds and cultures. ;We’re committed to increase diversity, ensure equal representation, and make Vodafone a place everyone feels safe, valued and included.

If you require any reasonable adjustments or have an accessibility request as part of your recruitment journey, for example, extended time or breaks in between online assessments, please refer to https://careers.vodafone.com/application-adjustments/ for guidance.

Together we can.

• Co-creating and deploying advanced machine learning and artificial intelligence solutions that drive revenue growth, cost optimization, customer retention, and enhanced customer experience.
• Developing big data monetization propositions and managing their end-to-end execution to unlock commercial value from customer data and behavioral insights.
• Delivering actionable customer insights, predictive analytics, and segmentation strategies that support M-Pesa objectives, commercial targets, and business decision-making.
• Designing and optimizing personalized customer lifecycle campaigns, including acquisition, activation, retention, reactivation, cross-sell, and upsell initiatives.
• Driving customer value enhancement through time to action, causal inference, difference in difference propensity modelling, churn prediction and customer journey optimization initiatives.
• Measuring and improving campaign effectiveness through Causal Inferences, advanced analytics, experimentation, and continuous optimization of customer engagement strategies.
• Promoting effective data governance, data quality, and responsible use of customer information while ensuring compliance with regulatory and privacy requirements.
• Causal Inference, Transformer Based Modelling, Classical ML Modelling its Lifecycle.
• ETL, Feature Engineering Alternatives & Embedding Infrastructure – AI Product Lifecycle Strategy
• Explainable AI (XAI) & Model Governance
• E2E go to market execution for big data and special projects propositions.
• Identify Analyze and Interpret trends or patterns in complex data sets.

• Advanced analytics
• Machine learning
• Customer intelligence
• Data-driven CVM strategies
• AI solutions
• Big data monetization
• Customer insights
• Predictive analytics
• Segmentation strategies
• Personalized customer lifecycle campaigns
• Causal inference
• Difference in difference propensity modelling
• Churn prediction
• Customer journey optimization
• Campaign effectiveness measurement
• Experimentation
• Data governance
• Data quality
• Responsible use of customer information
• Regulatory and privacy compliance
• Transformer Based Modelling
• Classical ML Modelling
• ETL
• Feature Engineering
• Embedding Infrastructure
• AI Product Lifecycle Strategy
• Explainable AI (XAI)
• Model Governance
• E2E go to market execution
• Trend analysis
• Pattern interpretation
• Analytical reasoning
• Logical reasoning
• Causal reasoning
• Stakeholder management
• Customer dynamics anticipation
• Competitor dynamics anticipation
• Market dynamics anticipation
• Challenging the status quo
• SQL
• Data querying tools
• Python
• Spark
• Databricks
• Big Data platforms
• Cloud analytics environments
• Machine learning frameworks
• Fintech ecosystems
• Predictive model deployment

• Bachelor’s Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Actuarial Science, Artificial Intelligence, or a related quantitative discipline.
• Master’s Degree in Data Science, Analytics, Artificial Intelligence, Business Analytics, or MBA (preferred).
• Minimum 5–7 years’ experience in Big Data Analytics, Data Science, Customer Analytics, CVM, Machine Learning, or Commercial Analytics.
• Advanced proficiency in SQL and data querying tools.
• Experience using Python, Spark, Databricks, or equivalent analytics tools.
• Experience working with Big Data platforms, cloud analytics environments, and machine learning frameworks.
• Strong understanding of Fintech ecosystems.
• Experience developing and deploying predictive models in a production environment.