Senior Data Scientist, Price Forecasting

About Electricity Maps

Electricity Maps is the world's most comprehensive electricity data platform, covering real-time, historical, and forecast data on electricity generation mix, power flows, carbon intensity, and prices.

Electricity Maps's work spans two distinct areas.

Sustainability: Clean, structured grid data for emissions reporting and carbon-aware decisions is used by companies like Google, Microsoft, Cisco, and Schneider Electric across 350+ zones with history back to 2015.

Power markets: Price and grid-fundamentals forecasts are provided for traders, battery operators, and asset optimisers managing their exposure to energy markets.

What ties it together is the methodology.

Electricity Maps standardises 100+ sources into one schema. Electricity Maps flow-traces electricity across borders and publishes signals that reflect what a region actually consumed, not just what it generated. That physical picture of the grid is the foundation everything is built on.

About the role

Electricity Maps runs short-term price forecasts at 15-minute granularity up to 72 hours ahead. There are always more signals to find and more accuracy to unlock. Price forecasting is the focus, and it draws on the same grid fundamentals data that underpins everything Electricity Maps builds.

You will be hands-on in Electricity Maps's models, exploring which signals are worth investigating and which features to build. You will also help evaluate what to look into and what not to. Day to day, that means working on day-ahead market modelling: evaluating new inputs, iterating on existing models, and shipping things that actually run in production, amongst other things.

You will be part of Electricity Maps's Data team, collaborating closely across forecasting, modelling, and market expertise. Direction is set together, but you own the execution, from idea to production. Electricity Maps works closely with the commercial team. Through them, you get direct feedback from prospects and customers, a loop that feeds back into model improvements and keeps Electricity Maps delivering value.

Electricity Maps moves fast, stays pragmatic, and keeps it lean so you can spend your time on the work that matters.

What You'll Do

• Build and improve short-term (72h) price forecasting models for the day-ahead market.
• Evaluate and prioritise new signals, asking: Is this worth adding? How does Electricity Maps forecast it? How does Electricity Maps integrate it? What features does Electricity Maps derive from it?
• Debug and improve existing models when you spot gaps or degradation in production.
• Collaborate across the Data team to shape what Electricity Maps investigates next.
• Surface modelling insights that connect back to real product and customer decisions.
• Own model performance end-to-end, from experimentation through to what is running live.

What You Won't Do

• Work from a fully scoped spec; Electricity Maps figures out what to build as much as how.
• Focus purely on research without caring about whether it ships and works in production.
• Operate in a siloed team disconnected from the product or the customer.

What Electricity Maps is Looking For

Must-haves

• Hands-on experience building price forecasting models for the day-ahead market.
• Strong intuition for feature engineering and signal selection in time-series and market contexts.
• Comfortable making calls on what to try, what to drop, and what to dig into, and owning those decisions.
• Comfortable owning model performance in production, not just in development.
• An understanding of how power market participants (traders, BESS optimisers, grid operators) actually use price forecast data.
• Able to communicate model behaviour and limitations clearly to non-technical stakeholders.

Nice-to-haves

• Experience with probabilistic forecasting (prediction intervals, quantile regression).
• Familiarity with European power market mechanisms (EUPHEMIA, EPEX, Nord Pool).
• MLOps experience: model monitoring, retraining pipelines, drift detection.
• Familiarity with ENTSO-E data and weather forecast data as forecasting inputs.

Electricity Maps's Stack

Relevant to this role: GCP, Python, Pandas, Polars, Scikit-learn, MLFlow, BigQuery, Modal.

You do not need to be an expert in all of these. Electricity Maps cares more about how you think and learn than your exact tool history.

Location: Copenhagen (in-office with flexibility)

Type: Full-time

Compensation: Competitive salary plus stock options.

Benefits: 6 weeks vacation, health insurance, annual company offsite, lunch, and snacks at the office.