Data Scientist

Companion.energy; Gent; Sur place; EUR 70 000 - 120 000; Plein temps

Résumé du poste

Companion.energy is hiring a Data Scientist to develop forecasting, optimization, and control algorithms at the core of its platform for large enterprises. You will work on energy demand forecasting, market price prediction, and real-time asset control, enabling second-by-second decisions that move megawatts and billions in value.

You will implement models and controllers, deploy them to production, and work with a small team to shape the data, ML, and control strategy for a flexible, renewable

Qualifications

• Background in ML and forecasting for time-series data.
• Experience with deployment of ML models in production.
• Familiarity with energy systems and markets is a plus.

Responsabilités

• Develop forecasting models for energy demand and market prices.
• Design control algorithms for real-time asset dispatch.
• Build optimization pipelines under uncertainty across portfolios.
• Deploy models to production and monitor performance.

Connaissances

Time series forecasting

Machine learning

Statistical modeling

Python

Real-time decision-making

Optimization

Model predictive control

Stochastic optimization

Outils

pandas

NumPy/SciPy

scikit-learn

PyTorch

Description du poste

We help large enterprises cut energy costs and valorize flexibility by steering their energy use in sync with market prices and renewable generation.

We're hiring a Data Scientist to develop the forecasting, optimization, and control algorithms at the core of Companion's platform.

You'll work on energy demand forecasting, market price prediction, and real-time asset control: models and algorithms that drive second-by-second decisions worth millions.

About Companion.energy

Companion.energy connects the financial side of energy management (contracts, markets, risk) with the operational side (assets, processes, sites). We model complex energy contracts and flexible assets, forecast demand and production, and translate predictions into automated, second-by-second control decisions that move megawatts and money. Our software is used by large B2B enterprises and energy players to lower OPEX, maximize revenue, increase renewable usage, and manage risk.

Why Companion.energy?

• Work on hard problems: Real-time forecasting, optimization, and closed-loop control in complex, stochastic energy systems. Your models and controllers move megawatts.
• See your work in production: Models you build get deployed and measured against real financial outcomes daily. Controllers you design steer real assets in real time.
• Join early, help shape the future: Small team, big impact. You'll shape Companion's data, ML, and control strategy.
• A mission that matters: Better forecasting and smarter control accelerate the transition to a flexible, renewable grid.

What you'll work on

• Forecasting models for energy demand, renewable production, and market prices — including the uncertainty estimates that feed downstream control.
• Control algorithms that translate forecasts and market signals into real-time asset dispatch decisions (battery steering, load shifting, balancing market participation), accounting for the inherent stochasticity of prices, generation, and demand.
• Optimization pipelines that schedule flexibility across portfolios of assets and markets under uncertainty.
• Closing the loop: connecting prediction, planning, and execution into systems that operate autonomously in a stochastic environment.

What we are looking for

• Strong foundation in machine learning, time series forecasting, and statistical modeling.
• Experience with control algorithms: model predictive control (MPC), stochastic optimal control, or similar approaches to real-time decision-making under uncertainty and constraints.
• Proficiency in Python and the standard data/scientific computing stack (pandas, NumPy/SciPy, scikit-learn, PyTorch or equivalent).
• Experience with short-term power or flexibility trading
• Experience working with real-world, messy time series data.
• Comfort deploying models and control algorithms in production environments, not just notebooks.
• Interest in or knowledge of energy systems: electricity markets, load forecasting, asset dispatch, balancing mechanisms, or related domains.
• Pragmatic approach: you understand the difference between a theoretically optimal solution and one that ships and delivers value under real-world uncertainty.
• Experience with mathematical optimization (linear/quadratic programming, mixed-integer formulations, stochastic programming) is a strong plus.

How we hire

Intro call → technical case → follow-up conversations with the team and founders.

We keep it simple and can move fast.