Founding Applied ML / Quant Forecasting Engineer

Elcast.ai builds forecasting, ranking, and decision systems for European power-market trading, focusing on identifying attractive opportunities in JAO month-ahead transmission rights auctions. The platform integrates data from ENTSO-E, JAO, third-party forecasts, Trading Economics, FX, holidays, and Copernicus medium-term weather data.

• Strong understanding of economically real prediction problems affecting business outcomes.
• Ability to learn specialized market mechanics and convert them into robust forecasting and decision logic.
• Experience working with hierarchical market entities such as countries, bidding zones, corridors, and auction products.
• Deep hands-on experience with modern machine learning for structured, time-dependent data, especially gradient boosting.
• Strong command of time-aware backtesting, no-leakage evaluation, walk-forward validation, and advanced model-quality metrics.
• Ability to build and evaluate forecasting, ranking, and decision-support models including learning-to-rank and uncertainty estimation.
• Understanding of financial modeling, portfolio selection, capital allocation, drawdown, false-positive cost, and risk-adjusted return.
• Ability to design and review feature engineering at scale including lagged, rolling, aggregated, hierarchical, and cross-source features.
• Senior-level Python engineering skills beyond notebook-based modeling.
• Experience with MLflow or similar experiment-tracking/model-lineage tools.
• Familiarity with Pydantic-driven configuration and type-driven architecture.
• Comfort with modular pipelines, strategy patterns, registries, tests, and refactoring without behavioral drift.
• Excellent SQL fluency including schema-qualified queries, function-based pipelines, bulk operations, and performance-aware data access.
• Ability to review and improve code for correctness, maintainability, reproducibility, security, and collaboration safety.
• Strong business orientation and ability to work in a founder-led, high-accountability environment.
• Low-ego, facts-first, productivity-first working style.
• Ability to inherit prior work and collaborate respectfully to achieve trading readiness in approximately three months.
• Ability to work productively with AI-assisted development while independently reviewing generated code.

Preferred qualifications:

• Experience in energy, commodities, quant systems, auctions, optimization, or markets with immediate economic consequences.
• Experience with portfolio selection, capital allocation, optimization, or risk layers on top of model outputs.
• Experience with unit testing, integration testing, model monitoring, model registries, and strong repository hygiene.
• Comfort with modern packaging, command-line tooling, and production-ready project structure.
• Ability to write clear architecture notes explaining system design decisions.

• Take technical ownership of the ML, forecasting, ranking, and decision-system layer of Elcast.ai.
• Challenge current models for leakage risk, weak assumptions, unstable features, overfitting, and backtest artifacts.
• Improve validation and reproducibility standards to accurately represent live-trading conditions.
• Develop, validate, and deploy forecasting, ranking, uncertainty, and portfolio-selection methods to optimize risk-adjusted trading returns.
• Improve modularity, interfaces, tests, and guardrails without unnecessary reinvention or behavioral drift.
• Collaborate daily with the founder, data scientist, and ETL pipeline engineer to advance the platform.
• Help define standards for experimentation, model promotion, code review, monitoring, documentation, and AI-assisted development.