Forecast Software Engineer - Statistical Modleing & Optimization

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.

• 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

• 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 Num Py, Pandas, Sci Py, 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

• 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

• 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

• 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