Senior Data Analyst - WENDIExpiring Soon!

• Analyse customer and transaction data to track wallet performance across acquisition, activation, usage, retention and churn.
• Develop customer segments based on behaviour, transaction patterns, value and channel usage to inform targeted campaigns.
• Develop and maintain predictive models for churn, dormancy, lifetime value and cross-selling opportunities.
• Design and evaluate A/B tests, cohort and funnel analyses to assess product, pricing and campaign effectiveness.
• Forecast transaction volumes, revenue and agent and merchant liquidity requirements to support business planning.
• Analyse transaction patterns and support development of rules and models to detect fraud and anomalies.
• Gather reporting requirements and build dashboards and automated reports with timely insights.
• Validate wallet data, support transaction and balance reconciliations, and resolve data quality issues.
• Analyse failed transactions, system downtime and complaint trends to identify service gaps and recommend fixes.
• Present evidence-based insights and recommendations to the wallet product team and senior management.
• Collaborate with Marketing, Product and Customer Service teams to translate insights into action.

Qualifications & Experience:

• Bachelor's degree in Computer Science, Data Science, Statistics or a related field from a recognised institution.
• Master's degree in Data Science, Computer Science or a similar discipline is advantageous.
• Formal training in machine learning, artificial intelligence, data mining or statistical modelling.
• Certification in MS Power BI, Tableau or Qlik Sense is an added advantage.
• Minimum of five years' experience as a Data or Business Analyst, ideally in banking, mobile money, payments or fintech.
• Hands-on experience analysing high-volume transaction data and understanding of digital wallet or mobile money operations.

Skills & Competencies:

• Strong SQL and Python or R for data analysis and modelling.
• Proficiency in at least one BI tool, Power BI or Qlik Sense.
• Solid statistics including hypothesis testing, regression and experimental design.
• Practical experience with machine learning techniques.
• Good grasp of data quality and validation practices.