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
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