Hybrid Lead, Renewable Forecasting & AI/ML Analytics

Electric Reliability Council of Texas, Inc.; Taylor (TX); Hybrid; USD 145,000 - 200,000; Full time

Job summary

Electric Reliability Council of Texas, Inc. (ERCOT) in Taylor, TX, is seeking a Supervisor of Renewable Forecasting and Analysis to lead a team of engineers and analysts.

You will direct AI/ML-enhanced wind and solar forecasts, manage forecast vendors, and oversee forecast reports and telemetry quality. The role includes coordinating with meteorologists and control room staff, managing the ERCOT Forecast Presentation Platform, and evolving Pay-for-Performance methods within ERCOT methodologies.

Qualifications

• Bachelor's degree in Electrical Engineering or related field is required.
• Master's degree in Electrical Engineering or related field is preferred.
• Professional licensure (PE) is preferred.

Responsibilities

• Directly supervise a team of ERCOT engineers and analysts, including performance management, coaching, and workload prioritization.
• Manage forecast vendor procurement and contract lifecycle (RFPs, renewals, SLAs).
• Lead development of operationally reliable wind and solar forecasts using AI/ML techniques.
• Oversee forecast performance analysis and reporting.
• Coordinate with meteorologists and control room staff on forecast matters.
• Own ERCOT Forecast Presentation Platform (FPP).

Skills

Leadership

Forecasting

AI/ML

Team coordination

Contract management

Education

Bachelor's in Electrical Engineering

Master's in Electrical Engineering

Tools

Forecast Platform

RFPs

Job description

Electric Reliability Council of Texas, Inc. (ERCOT) in Taylor, TX, is seeking a Supervisor of Renewable Forecasting and Analysis to lead a team of engineers and analysts.

You will direct AI/ML-enhanced wind and solar forecasts, manage forecast vendors, and oversee forecast reports and telemetry quality. The role includes coordinating with meteorologists and control room staff, managing the ERCOT Forecast Presentation Platform, and evolving Pay-for-Performance methods within ERCOT methodologies.