Role purpose: 

To develop fit-for-purpose predictive and affordability models, execute and analyse our markets to determine the most effective way to increase customer base whilst minimizing customers at risk of churning, increase customer acquisition, conversion and retention; cross selling and upselling, customer behaviour segmentation, analyse customer feedback from call centre, social network analysis. Increase of M-Pesa product penetration, customer engagement, increase M-Pesa ARPU and sharing insights.

Key accountabilities 

• Use an agile approach to determine the best use of data to develop predictive models and perform analysis in line with business requirements (e.g. customer churn; customer acquisition and retention; cross selling and upselling; customer behaviour segmentation; analyse customer feedback from call centre, social network analysis; etc.)
• Develop data processes to mine value from a variety of data sources that include high-volume, transactional-level data.
• Provide analytical support to various other business teams.
• Collaborate with reporting team, business development team, product engineers, and CBU where necessary to facilitate data analysis.

Core competencies, knowledge and experience

• Domain knowledge and passionate about the mobile money business, curious about data.
• A mixture of multidisciplinary skills ranging from an intersection of computer science, mathematics, statistics, communication and business.
• Experience in mining and extracting valuable information from large data sets.
• Experience in mobile money data analytics and reporting.
• Machine learning and statistical modelling (supervised and unsupervised learning)

Technical / professional qualifications: 

• A degree in Mathematics, Statistics, Computer science, Data Science or similar. 
• Programming and Database: Python, r, SQL and NoSQL, Oracle;  
• Advanced excel.