Olutomiwa (Tommy) Oloyede

Managed real-time power desk operations, leveraging advanced data analytics and automation tools to optimize market participation and ensure regulatory compliance for a leading energy provider.

• Participated actively across PJM, MISO, ERCOT, NYISO, ISONE, and IESO real-time and day-ahead power markets, ensuring optimal trading and scheduling.
• Streamlined and automated critical reporting processes by developing solutions with Microsoft Power Apps, Azure Blob Storage, and Azure Data Factory, enhancing operational efficiency.
• Engineered prediction modeling and offer submittal tools for Independent System Operators using Python, VBA, and C#, significantly improving market response capabilities.
• Conducted robust real-time data analysis using Monarch and PI Datalink to identify patterns, trends, and anomalies in power grids, informing strategic decisions.
• Contributed to a seamless cloud migration initiative, facilitating smooth transitions for customers and stakeholders while maintaining business continuity.
• Oversaw and managed a $250K monthly fee-based business, demonstrating strong financial acumen and operational oversight.

Guided students in data projects and led a team of learning assistants, fostering skill development in programming and data science.

• Assisted students with complex data projects in Excel, Python, SQL, JavaScript, ETL, and Machine Learning, enhancing their technical proficiency.
• Led a team of Learning Assistants, providing guidance and support to resolve bugs in student projects, improving overall project quality.

Led regulatory and strategic initiatives, managing critical annual processes and analyzing financial transmission rights to optimize hedge effectiveness for a major utility network.

• Managed the annual Auction Revenue Rights nomination process for all operating companies within the Entergy Network, ensuring compliance and maximizing revenue opportunities.
• Directed the annual Reserve Source Point process for all operating companies in the Entergy Network, covering a footprint exceeding 15,000 MW/Hr.
• Analyzed congestion exposure and Financial Transmission Rights (FTR) using Python and Machine Learning to accurately measure hedge effectiveness.
• Prepared comprehensive reports for the Vice President on the effectiveness of the FTR hedge program, providing data-driven insights for executive decision-making.

Performed diverse financial analyses and power scheduling, ensuring compliance and optimizing wholesale power purchases for Entergy Wholesale Commodities.

• Conducted extensive financial analyses for Commercial Operations and Origination using Excel, VBA, and SQL, supporting data-driven decision-making.
• Researched and documented key projects, including Fairwinds and Project Holland (Nuclear Decommissioning), collaborating with stakeholders to ensure scope integrity for large-scale initiatives.
• Scheduled wholesale power purchases on nuclear, natural gas, and coal assets within Entergy MISO and NYISO, totaling 4,000MW/hour/day, in compliance with FERC and NERC regulations.
• Developed a new financial model for EAM assets and introduced a billing process that generated approximately $80,000 in monthly savings for the group.
• Ensured continuous compliance with applicable market and operational rules of FERC and NERC, mitigating regulatory risks.

Analyzed market data and financial performance to advise management on strategies, significantly improving sales and district revenue.

• Presented monthly market analysis to the management team via PowerPoint, advising on cost-effective strategies to capitalize on higher margins and volume.
• Increased sales in the Pearland district by over 30% compared to the previous year through effective financial analysis of individual store P&Ls.
• Contributed to district revenue generation exceeding $1MM/month, demonstrating direct impact on financial performance.

A software analysis leveraging big data from the Energy Information Administration to predict energy production and consumption in Texas and California for the next 23 years using Machine Learning algorithms.

A data analytics tool for E&P and service companies to compare actual and historical oil and gas wells in Loving County-Midland Basin, evaluating drilling success and productivity.