Naiya Khalid

• Developed a 7-day electricity price forecasting system using Portuguese energy market data to support analytics-
driven pricing insights and decision-making.
• Integrated electricity prices, weather, energy load, and renewable generation data into unified analytical datasets
that improved model training and enabled end-to-end forecasting.
• Designed and optimized a LightGBM forecasting model achieving MAE of 0.0824 through feature engineering,
model experimentation, and evaluation.
• Developed scalable end-to-end ML workflows with FastAPI integration to automate prediction and analytics
processes.
• Created user-oriented monthly savings estimation features by transforming forecasting outputs into personalized
consumption and pricing insights.
• Identified key drivers of electricity price volatility through exploratory data analysis, revealing relationships between
renewable generation, demand, and market prices.
• Generated structured reports to support operational decision-making, improving operational visibility and
supporting business performance monitoring.

• Managed onboarding, documentation, and compliance workflows for trainees and internal stakeholders.
• Improved onboarding accuracy by 30% by streamlining documentation processes and communication workflows.
• Coordinated internal communication channels and generated analytical reports and dashboards that supported
operational decision-making and improved visibility into business performance.
• Supported process tracking and reporting across distributed teams, ensuring documentation accuracy and
improving cross-functional collaboration in a remote environment.