Senior Data Scientist

h3TL;DR /h3 /brpWe help large enterprises cut energy costs and valorize flexibility by steering their energy use in sync with market prices and renewable generation. /p /brpWe're hiring a Senior Data Scientist to develop the forecasting, optimization, and control algorithms at the core of Companion's platform. /p /brpYou'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. /p /brh3About Companion.energy /h3 /brpCompanion.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. /p /brh3Why Companion.energy? /h3 /brul /brliWork on hard problems: Real-time forecasting, optimization, and closed-loop control in complex, stochastic energy systems. Your models and controllers move megawatts. /li /brliSee 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. /li /brliJoin early, help shape the future: Small team, big impact. You'll have a large say in how we forecast, optimize, and control. /li /brliA mission that matters: Better forecasting and smarter control accelerate the transition to a flexible, renewable grid. /li /br /ul /brh3What you'll work on /h3 /brul /brliForecasting models for energy demand, renewable production, and market prices — including the uncertainty estimates that feed downstream control. /li /brliControl 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. /li /brliOptimization pipelines that schedule flexibility across portfolios of assets and markets under uncertainty. /li /brliThe data path that feeds all of the above: ingesting and cleaning messy asset and market time series, handling gaps and backfills, quality checks, and the orchestrated pipelines (we run Dagster) that keep models fed and predictions flowing on schedule. /li /brliClosing the loop: connecting prediction, planning, and execution into systems that operate autonomously in a stochastic environment. Concretely, this includes building the backtesting and measurement harness that tells us whether a model is earning money in production — and keeps telling us once it starts to drift. /li /br /ul /brpTo be explicit about what this means at our size: there is no data team behind you and no MLOps engineer to hand a model to. You own the path from raw data to a running, monitored model. That is real ownership over something that matters, and it is not a fit for someone who wants to work only in notebooks. /p /brh3What we are looking for /h3 /brul /brliDeep experience with time series forecasting and statistical modeling on messy, real-world data. /li /brliProficiency in Python and the standard data/scientific computing stack (pandas, NumPy/SciPy, scikit-learn, PyTorch or equivalent), at a level where your code goes to production and stays there. /li /brliModels you have shipped, measured against real outcomes, and later had to change. /li /brliComfortable owning the data path end to end: SQL, and orchestration with Dagster, Airflow, Prefect or equivalent, including the discipline to keep scheduled pipelines healthy rather than just running. /li /brliYou can explain a modeling decision to an engineer who doesn't share your maths background, and write down your assumptions clearly enough that someone else can safely change your model. /li /brliPragmatic approach: you understand the difference between a theoretically optimal solution and one that ships and delivers value under real-world uncertainty. /li /br /ul /brh3Strong plusses /h3 /brul /brliControl algorithms for real-time decision-making under uncertainty and constraints: model predictive control (MPC), stochastic optimal control, or similar. /li /brliMathematical optimization: linear/quadratic programming, mixed-integer formulations, stochastic programming. /li /brliKnowledge of energy systems: electricity markets, load forecasting, asset dispatch, balancing mechanisms, or related domains. /li /br /ul /brh3Location /h3 /brpGhent, Belgium (hybrid) /p /brh3How we hire /h3 /brpIntro call → technical case → follow-up conversations with the team and founders. /p /brpWe keep it simple and can move fast. /p