• Leveraged expertise in Pandas, NumPy, Matplotlib, Seaborn, Plotly, Sklearn, & ML algorithms to extract actionable insights from complex data & condense findings into simple conclusions to communicate to stakeholders
• Identified faulty metering among 400+ energy meters by cleaning & analyzing chilled water, steam, electricity data, & applied statistical analysis techniques to pinpoint anomalies & address infrastructure issues before equipment failure
• Uncovered trends in labor workload data for Facilities Management tasks through in-depth EDA & insightful visualizations in Python, improving resource allocation, task prioritization, & overall efficiency by 10%
• Streamlined invoice processing by automating comparisons to energy usage data, saving billing team 10+ hours per month