Associate Director - ERCOT Market Subject Matter Expert (Part-Time/ Contract)

Nagarro•Remote, REMOTE

About The Position

Nagarro is seeking an experienced ERCOT Market Subject Matter Expert to support a focused Proof of Concept for congestion driver attribution across the ERCOT nodal network. The engagement aims to develop a Graph Neural Network-based solution that combines physical power-system fundamentals, market participant behaviour, and ERCOT transmission-network topology to identify and explain the key drivers of congestion. The ERCOT SME will work closely with Graph ML engineers, data engineers, power-market analysts, and project leadership to ensure that the analytical models are grounded in ERCOT market principles and produce interpretable, actionable outputs.

Requirements

Demonstrated professional experience working with the ERCOT wholesale electricity market, with a strong understanding of congestion modelling and nodal market operations.
Practical knowledge of: Locational Marginal Pricing and congestion components, Shift factors and transmission-interface exposure, Binding constraints, contingencies, and constraint shadow prices, Day-Ahead and Real-Time Market operations, Generation, load, and transmission outages, ERCOT bid-and-offer disclosures, Congestion Revenue Rights and related market information.
Experience analysing historical congestion events and identifying the underlying physical, transmission, or market-behaviour drivers.
Experience in one or more areas such as power-market trading, congestion analytics, forecasting, market simulation, production-cost modelling, power-flow analysis, or transmission-network modelling.
Familiarity with ERCOT datasets, including network-model files, outage reports, market disclosures, constraint reports, and other publicly available market information.
Ability to translate complex power-market concepts into clear requirements and validation criteria for data engineering, analytics, and machine-learning teams.
Experience collaborating with data scientists, machine-learning engineers, trading teams, or advanced analytics stakeholders.
Strong analytical, communication, and stakeholder-management skills.

Nice To Haves

Exposure to nodal price forecasting, explainable AI, graph-based analytics, or AI-led power-market applications would be advantageous.

Responsibilities

Providing domain expertise on ERCOT market operations, congestion mechanisms, and nodal pricing.
Guiding the interpretation of transmission constraints, shift factors, shadow prices, binding intervals, and congestion propagation.
Supporting the definition and validation of congestion-driver categories.
Translating ERCOT market behaviour into functional and analytical requirements for the data science and Graph ML teams.
Ensuring that model outputs are understandable and relevant to power-market analysts and trading stakeholders.
Validating congestion attributions against independently verifiable historical ERCOT market events.
Supporting the assessment of the model’s readiness for future nodal price-forecasting use cases.
Explain ERCOT nodal market design, settlement-point pricing, transmission congestion, and Locational Marginal Pricing components.
Analyse binding transmission constraints, contingency conditions, shift-factor exposures, shadow prices, and historical binding hours.
Support the identification of congestion caused by generation outages, renewable oversupply, load concentration, transmission outages, contingencies, and market participant behaviour.
Interpret participant-level and aggregated bid-and-offer disclosures within the context of congestion and shadow-price formation.
Work with the Graph ML team to define appropriate node, edge, transmission, market, and temporal attributes for the ERCOT network graph.
Review the use of ERCOT transmission models, shift-factor matrices, contingency files, line ratings, outage feeds, market disclosures, and historical congestion information.
Define a practical taxonomy for congestion-driver attribution.
Support the separation and interpretation of physical and behavioural contributors to observed congestion.
Help establish business rules, assumptions, thresholds, and domain constraints for model development.
Validate model-generated congestion attributions against known historical events, including documented unit outages, transmission outages, curtailment events, and contingency-driven constraints.
Review propagation paths and assess whether identified node and interface impacts are electrically and commercially plausible.
Evaluate the accuracy and usefulness of model explanations, confidence scores, and shadow-price attribution.
Participate in back-testing reviews and assist in comparing model performance against baseline approaches.
Ensure that model outputs can be interpreted by market analysts without requiring advanced machine-learning knowledge.
Collaborate with the client’s trading, analytics, and power-market teams during architecture reviews and validation checkpoints.
Participate in regular working sessions with Nagarro’s Graph ML engineers, data engineers, and project leadership.
Present findings, assumptions, limitations, and recommendations in clear business and market terminology.
Support risk identification and timely escalation of issues related to market data, modelling assumptions, or ERCOT-specific interpretation.