Michelle Spandri

• Developed a similarity algorithm on Azure to identify related ServiceNow tickets, optimizing issue resolution and reducing redundancy.
• Developed machine learning models in Python using AWS SageMaker to predict KPIs.
• Designed a Power BI dashboard leveraging Snowflake data to track financial growth, automate reporting, and enhance executive decision-making.
• Implemented machine learning models to detect fraudulent requests, leveraging synthetic data to improve model performance, reducing financial losses and enhancing fraud prevention capabilities.
• Developed a Python-based application to automate travel allowance calculations for truck drivers, integrating data import via API and manual input.
• Created optimization algorithms for store-to-store transfers, balancing profit maximization and CO2 emissions reduction, with an interactive Shiny visualization for strategic decision-making.
• Developed a Python tool for automated anomaly detection in databases, improving data integrity and reducing manual monitoring efforts.
• Forecasted highway traffic flows by selecting the best forecasting model and relevant features, optimizing resource allocation and improving traffic management efficiency.
• Designed and implemented a Power BI dashboard to track purchase orders in real time, integrating SAP BW/4HANA and SharePoint, enhancing procurement efficiency and supply chain visibility.

Monitored audience engagement in real-time using camera data, providing insights on customer behavior and store performance. Worked with marketing teams to optimize ad placements based on heatmaps, ensuring compliance with GDPR and privacy regulations.

Evaluated alternative investment instruments by analyzing their returns and volatility compared to traditional portfolios. Conducted risk-adjusted performance analysis and built models to simulate portfolio outcomes. Presented insights to senior analysts to support strategic allocation decisions.