Responsible for leveraging advanced analytics, machine learning, and customer intelligence to develop data-driven customer value management strategies that drive customer acquisition, engagement, retention, product penetration, and revenue growth across the M-Pesa business.
Responsibilities
- Co-create and deploy advanced machine learning and artificial intelligence solutions that drive revenue growth, cost optimization, customer retention, and enhanced customer experience.
- Develop big data monetization propositions and manage their end-to-end execution to unlock commercial value from customer data and behavioral insights.
- Deliver actionable customer insights, predictive analytics, and segmentation strategies that support M-Pesa objectives, commercial targets, and business decision-making.
- Design and optimize personalized customer lifecycle campaigns, including acquisition, activation, retention, reactivation, cross-sell, and upsell initiatives.
- Drive customer value enhancement through time-to-action, causal inference, difference-in-difference propensity modelling, churn prediction, and customer journey optimization initiatives.
- Measure and improve campaign effectiveness through causal inference, advanced analytics, experimentation, and continuous optimization of customer engagement strategies.
- Promote effective data governance, data quality, and responsible use of customer information while ensuring compliance with regulatory and privacy requirements.
- Lead causal inference, transformer-based modelling, classical machine learning modelling, and model lifecycle management.
- Manage ETL, feature engineering alternatives, embedding infrastructure, and AI product lifecycle strategy.
- Implement explainable AI and model governance.
- Manage end-to-end go-to-market execution for big data and special project propositions.
- Identify, analyze, and interpret trends or patterns in complex data sets.
Requirements
- Advanced analytics and customer intelligence
- Machine learning and artificial intelligence
- Causal inference and predictive modelling
- Transformer-based modelling and classical machine learning
- SQL and data querying
- Python, Spark, and Databricks
- Big Data platforms and cloud analytics environments
- Machine learning frameworks
- Data governance, data quality, and model governance
- Explainable AI
- Customer value management and customer lifecycle analytics
- Fintech ecosystem knowledge
- Stakeholder management
- Analytical, logical, and causal reasoning
Application Instructions
- For accessibility or reasonable-adjustment requests, see
