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About Gryd
Gryd's mission is simple: solar and battery systems in every new-build home. Fully funded. No upfront cost to developers or homebuyers. And managed autonomously to deliver lower energy bills from day one.
We're embarking on a new project to build the technology that powers the next chapter — a platform that turns individual home solar-battery systems into a coordinated, intelligent energy network to minimise bills and reduce peak demand at critical grid nodes. It's novel work, it's funded, and it matters.
We're a small, focused team. We move fast, we care deeply about the work, and we're looking for people who bring the same energy.
The Role
This is a technically demanding, intellectually rewarding role — and you'll be leading it.
As Gryd’s first Data Scientist, you'll own the forecasting and decision making strand of our project. That means developing and validating models for household electricity demand, solar generation, and battery behaviour across clusters of new-build homes on new housing developments.
The challenge is real: new-build homes are data-sparse. No historical consumption data, variable occupants, novel system configurations. You'll need to build cold-start models using proxy and synthetic data, and iterate as live operational data accumulates across real, occupied homes.
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Your forecasts feed directly into the optimisation algorithms at the core of the platform. This is pivotal work — and you'll see the impact of it in the real world.
What You'll Be Doing
- Building forecasting models for household demand, solar generation, and battery state-of-charge in data-sparse environments
- Developing cold-start approaches using synthetic data generation, proxy datasets, and transfer learning
- Creating short-horizon forecasts (intra-day to multi-day) for use in real-time optimisation
- Validating model performance rigorously against clear baselines — and documenting it for reporting
- Iterating as live data accumulates across deployed developments
- Collaborating closely with our Technical Lead to make sure your outputs work in the optimisation engine and wider platform
- Contributing to performance analysis — cost savings, asset utilisation, carbon impact
- Keeping your work clean, documented, and explainable throughout
What We're Looking For
You'll need:
- Strong experience in time-series forecasting, probabilistic modelling, and feature engineering
- Proven ability to build models where data is limited or cold-start
- Hands-on Python — scikit-learn, statsmodels, Prophet, PyTorch or similar
- Experience building and maintaining real-world data pipelines
- A rigorous approach to validation, benchmarking, and documentation
- The ability to lead a technical workstream independently in a small, fast-moving team
- Energy sector experience — demand forecasting, solar generation modelling, or battery storage
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Helpful but not essential:
- Familiarity with smart meter data, half-hourly settlement data, or inverter/BMS telemetry from residential solar and battery platforms
- Understanding of distributed energy resources and grid flexibility
- Experience on grant-funded or academic-industry collaborative research
- Cloud infrastructure (AWS, GCP, or Azure)
What We Offer
- Competitive salary
- 18-month FTC — with potential to go permanent as Gryd scales
- Hybrid working 2-3 days a week in person, Central London office.
- Real technical leadership on company critical project
- A small team where your decisions matter and your work is visible
- Direct access to the founders and exposure across the whole business
- 25 days holiday plus bank holidays
Contract Details
The contract runs from August/September 2026 to approximately January 2028. We're growing fast and the right person will have a route to a permanent role.
1 month notice period. PAYE terms.
To apply, send your CV and a short cover email — no more than a couple of paragraphs — to hiring@gryd.energy.
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