About the Role
ABOUT THE ROLE Our Client is seeking a Forecast Software Engineer — Statistical Modeling & Optimization to build analytical software supporting ERCOT forecasting, benchmarking, and confidence scoring. Working with power-market, grid-physics, data, and software experts, this engineer will translate established methodologies and grid-model outputs into reliable production software. The role combines software engineering, applied statistics, time-series validation, optimization, and uncertainty measurement. RESPONSIBILITIES Develop ERCOT congestion, basis, and nodal-price forecasting services Implement simplified SCED, economic-dispatch, and constraint-aware optimization calculations Integrate grid topology, transmission constraints, generator characteristics, load and renewable forecasts, fuel curves, and market data Build rigorous historical replay and backtesting processes while preventing data leakage and look-ahead bias Measure forecast accuracy, bias, stability, calibration, and uncertainty using appropriate statistical methods Develop confidence-scoring methodologies based on model performance, data quality, forecast horizon, scenario volatility, model agreement, and other relevant factors Build standardized frameworks for comparing internal and third-party forecasts Develop ensemble-model comparisons, benchmark reports, and model-performance scorecards Implement scenario and sensitivity analysis for fuel prices, load growth, renewable output, generator availability, outages, and transmission constraints Develop documented APIs and batch-processing workflows for forecasting and confidence scores Write maintainable, tested, version-controlled Python software and automated unit, integration, regression, and statistical tests Document methodologies, formulas, assumptions, dependencies, limitations, and model versions REQUIRED QUALIFICATIONS Bachelor’s or master’s degree in statistics, applied mathematics, operations research, computer science, engineering, econometrics, data science, or a related field Strong production-level Python skills Strong foundation in applied statistics and probability Experience developing quantitative, analytical, or forecasting software Proficiency with NumPy, Pandas, SciPy, scikit-learn, SQL, and Git Experience with time-series validation, backtesting, uncertainty measurement, and model-performance analysis Ability to translate domain-expert requirements into reproducible software Strong testing, debugging, and technical-documentation skills PREFERRED QUALIFICATIONS Experience with Pyomo, Gurobi, CPLEX, OR-Tools, CVXPY, or similar optimization tools Experience with electricity markets, utilities, grid analytics, production-cost modeling, commodity forecasting, or energy trading Knowledge of LMPs, congestion, economic dispatch, SCED, generator constraints, and transmission systems Experience with ensemble models, probabilistic forecasting, or calibrated confidence measures Experience with PostgreSQL, REST APIs, Docker, and cloud deployment Knowledge of Monte Carlo simulation, Bayesian methods, quantile forecasting, bootstrapping, or similar uncertainty-estimation techniques ERCOT experience is valuable but not required WHAT SUCCESS LOOKS LIKE Operational ERCOT forecasting services integrated with the broader platform Reliable dispatch and constraint-aware forecasting capabilities Reproducible historical replay and backtesting Documented forecast-validation and confidence-scoring frameworks Consistent internal and third-party forecast benchmarking Scenario and sensitivity-analysis capabilities Forecast and confidence-score APIs Automated statistical and regression testing Clear documentation of methodology, assumptions, limitations, and performance DESIRED CHARACTERISTICS Statistically rigorous while remaining practical and delivery-focused Strong software engineering discipline Able to identify and challenge weak validation approaches Comfortable explaining uncertainty and statistical results to nontechnical audiences Collaborative with market, data, software, and engineering specialists Highly attentive to reproducibility, data lineage, and model governance Comfortable working in a fast-moving startup environment