Data Scientist, Energy Forecasting & Optimization

Gryd Energy; Greater London; Hybrid; GBP 70,000 - 90,000; Full time

Benefits offered by this job

18-month FTC

Hybrid work 2-3 days in Central London

Technical leadership on critical proj

Small team with visible impact

Direct access to founders

Job summary

Gryd Energy is building a scalable platform that turns home solar and battery systems into a coordinated energy network. As the first Data Scientist, you will own forecasting for household demand, solar generation, and battery behaviour, using synthesis data to overcome sparse data and guiding the model through live deployments.

You will lead a small, fast-moving team, collaborating with the Technical Lead to ensure the optimisation engine functions with robust, explainable outputs.

Qualifications

• Experience in time-series forecasting and probabilistic modelling.
• Strong Python skills and experience with ML libraries.
• Ability to lead a technical workstream in a small team.
• Energy sector experience in demand forecasting or solar/battery storage is a plus.

Responsibilities

• Build forecasting models for household demand, solar generation, and battery SOC in data-sparse environments.
• Develop cold-start approaches using synthetic data and transfer learning.
• Create short-horizon forecasts for real-time optimisation.
• Validate model performance against baselines and document results.
• Collaborate with Technical Lead to ensure outputs integrate with the optimisation engine.
• Contribute to performance analysis: cost savings, asset utilisation, and carbon impact.
• Keep work clean, documented, and explainable throughout.

Skills

Time-series forecasting

Probabilistic modelling

Feature engineering

Python

scikit-learn

statsmodels

Prophet

PyTorch

Data pipelines

Validation

Documentation

Leadership

Tools

AWS

GCP

Azure

Job description

Gryd Energy is building a scalable platform that turns home solar and battery systems into a coordinated energy network. As the first Data Scientist, you will own forecasting for household demand, solar generation, and battery behaviour, using synthesis data to overcome sparse data and guiding the model through live deployments.

You will lead a small, fast-moving team, collaborating with the Technical Lead to ensure the optimisation engine functions with robust, explainable outputs.