Maria Waqar

Built a machine-learning approach to segment customers and predict Customer Lifetime Value. Used modeling outputs to support more actionable targeting and business interpretation of customer groups. Documented methodology and results for review and demonstration.

Researched how transfer learning models perform under image noise conditions for pneumonia detection. Evaluated robustness behavior when data quality degrades, with the goal of understanding model reliability. Produced outputs suitable for academic-style presentation of findings.

Conducted a structured literature review of deep learning methods applied to precision agriculture. Summarized approaches and themes across the 2018–2025 period to map progress and common techniques. Focused on synthesizing model capabilities and use cases rather than building a single production system.

Developed a churn prediction modeling project focused on telecom customer behavior. Applied machine-learning methods to identify patterns related to churn risk. Packaged the work as a predictive analytics exercise with business relevance for retention decisions.

Owned sales planning and commercial analytics for the North Region, supporting near-complete achievement of commercial targets and contributing to revenue growth. Built sales forecasts using historical data with controlled variance, and created scenario and what-if models for annual planning. Developed KPI dashboards and performed contribution and Pareto analyses to identify performance gaps and guide corrective actions across teams.

Analyzed financial services data for forecasting, target allocation, and ongoing performance reporting. Created KPI reports to evaluate product performance and supported strategic decision-making through trend analysis. Managed retailer portfolio analytics, resolved operational issues to improve channel performance, and delivered training to stakeholders on systems and processes.

Provided system support for Siebel CRM and Tracker, addressing access and data issues for end users. Maintained system records and supported operational reporting needs across the department. Ensured continuity of user access and reliability of supporting data for day-to-day operations.