Open to opportunities

Hadija Halfani

@hadijahalfani

I turn customer, commercial, and operational data into decisions that improve revenue assurance and product performance.

Tanzania

Message

What I'm looking for

I'm looking to use customer, commercial, and operational analytics to improve products, customer experience, revenue assurance, and data-driven business decisions.

At Vodacom Tanzania, I lead Revenue Assurance for M-Pesa platform migration, safeguard transaction integrity, and build analytics that improve commercial controls. My near real-time TIPS 3-Way Match dashboard reduced manual Treasury reconciliations by over 70%, while a machine learning model helped reduce M-Pesa revenue leakage by 15%.

Previously, I translated digital product and customer-channel needs into technical solutions as a Business Analyst and led analytics delivery at AfroPavo Analytics, including customer segmentation for DCB Bank. I enjoy turning behavioural data, dashboards, and cross-functional workshops into practical actions that strengthen customer experience, profitability, and business growth.

Experience

Work history, roles, and key accomplishments

Current

Margin Assurance Analyst

Current

Vodacom Tanzania

Apr 2024 - Present (2 years 4 months)

Led the Revenue Assurance workstream for a business-critical M-Pesa platform migration, establishing validation criteria, control testing, and data integrity checks. Designed and implemented dashboards and predictive models that reduced manual reconciliations by over 70% and contributed to a 15% reduction in M-Pesa revenue leakage.

Revenue Assurance Data Integrity Controls Testing Dashboard Development Data Visualization predictive modeling Machine Learning SQL Power BI Tableau Python Data Quality Stakeholder Management Business Analysis

Education

Degrees, certifications, and relevant coursework

Coventry University

Master of Science, Data Science and Computational Intelligence

Pursued a Master of Science in Data Science and Computational Intelligence, with a research project evaluating ensemble bagging techniques against individual machine learning models for water potability prediction.