Key Tasks: 

 

1. Business Intelligence Governance & Infrastructure

• Lead the centralization of all Business Intelligence (BI) assets including dashboards, scorecards, and KPI tracking tools across departments.
• Ensure BI reporting standards, metadata definitions, and data governance protocols are consistently applied.
• Partner with data engineering teams to ensure availability, integrity, and optimization of underlying data structures.

2. Advanced Analytics & Machine Learning

• Build predictive models for churn, CLV (Customer Lifetime Value), ARPU optimization, and product recommendation engines.
• Apply advanced statistical and Machine Learning techniques (clustering, regression, survival analysis, gradient boosting, neural networks) for segmentation and retention.
• Deploy and monitor models in production environments, collaborating with MLOps and engineering teams.
• Lead A/B and multivariate testing frameworks to evaluate campaign, pricing, and product interventions.

3. Enterprise BI Strategy & Enablement

• Architect self-service BI ecosystems, empowering commercial and product teams to access governed, high-quality data.
• Standardize semantic layers and KPI definitions to create a single source of truth across the organization.
• Automate reporting pipelines and alerts for proactive performance management.

4. Performance Monitoring & Reporting

• Design, automate, and maintain enterprise-grade analysis and dashboards to monitor Price Per Minute (PPM), ARPU, and other core KPIs across voice, data, and digital services.      
• Measure campaign effectiveness, price elasticity, and product adoption.
• Perform advanced trend analysis and statistical modeling on performance metrics to identify patterns, anomalies, and elasticity drivers, enabling data-driven optimization of product portfolios, pricing strategies and CVM campaigns.
• Monitor and analyze KPI performance against budget and forecast targets, identifying underperforming products, segments, or geographies; conduct profitability assessments of investments, sites, and commercial initiatives to recommend corrective actions and optimize ROI.

5. Retention & Churn Management

• Support designing data-driven retention strategies by analyzing lifecycle behaviors, campaign effectiveness, and churn drivers across segments.

6. RGS (Revenue Generating Subscribers) Analysis

• Conduct advanced RGS segmentation and cohort analysis to evaluate subscriber monetization, lifecycle behaviors, and contribution to ARPU and margin.
• Track RGS performance vs. acquisition, retention, and churn targets, identifying high-value clusters and underperforming segments to guide commercial strategy.
• Measure profitability at subscriber and product level, linking RGS dynamics to investment decisions and ROI optimization.