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.
- Objectives of the Role
The ERCOT Market SME will be responsible for:
- 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.
- Key Responsibilities
ERCOT Market and Congestion Expertise
- 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.
Model and Data Support
- 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.
Validation and Interpretation
- 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.
Stakeholder Collaboration
- 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.
- Role 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:
o Locational Marginal Pricing and congestion components
o Shift factors and transmission-interface exposure
o Binding constraints, contingencies, and constraint shadow prices
o Day-Ahead and Real-Time Market operations
o Generation, load, and transmission outages
o ERCOT bid-and-offer disclosures
o 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.
- Exposure to nodal price forecasting, explainable AI, graph-based analytics, or AI-led power-market applications would be advantageous.
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