Data Scientist

Volter is a top-tier climate VC-backed B2B energy tech platform building the products that make clean, renewable power simple for businesses everywhere. The energy transition is a huge and vital opportunity, but it's currently too difficult for commercial & industrial companies to achieve. Historically, the energy industry has been too opaque, complex and lacking in trust. We’re fixing that. Our aim is to be the “Octopus Energy for industry”: a modern tech platform providing new renewable energy products with radical transparency — accelerating deployment of renewables and helping customers cut cost and carbon without complexity.

We’re excited to use technology to help our customers deploy & utilise more renewable energy and fast forward the energy transition - and we need your help!

You’ll lead our data science work, including owning forecasting and pricing optimisation: producing accurate half‑hourly demand and generation forecasts, shaping prices, quantifying uncertainty and translating it into simple decisions for customers and our supply portfolio. You’ll own the full model lifecycle—from problem framing and baselines to productionisation, monitoring and iterative improvement.

You are a self-starter who excels both independently and in a team. You dislike corporate structure and bureaucracy, and believe in a "best idea wins" culture. This is why you prefer a growth-oriented, flat, startup environment.

A desire to keep learning and having an impact is very important to you. You fear looking back in 10 years time and realising you never made a difference…that you were simply a tiny cog in a large machine that will churn on with or without you.

• Build, validate and deploy time‑series models (including probabilistic where useful) for customer demand and generation and constantly improve model performance
• Understand customer profiles and develop relevant features from HHD, weather, calendar and price inputs; set up robust back‑tests and error attribution
• Develop, maintain and improve pricing and trading decision engines to maximise PnL
• Alongside engineering, create reliable feature/data ETL pipelines, and partner with product to surface insights in the platform
• Create risk and hedging analytics (e.g., shape/volume error distributions, scenario analysis, what‑if tools)
• Design monitoring for model/data drift; manage model registries and experiment tracking
• Be responsible for driving new initiatives to improve business outcomes from a data science perspective and be able to drive and own from idea to scoping, production and maintaining
• Communicate results clearly to non‑technical stakeholders; build explainability into outputs

• 4–8+ years in Data Science/ML with a focus on time‑series forecasting and decision analytics
• Strong Python (pandas, NumPy, scikit‑learn, statsmodels; PyTorch/TF a plus) and SQL; rigorous evaluation (MAE/RMSE, MAPE, pinball loss)
• Experience productionising models (APIs/batch jobs), experiment tracking (e.g., MLflow) and basic MLOps
• Being able to evaluate the right tools for the job given business requirements & experiment with new ways of doing things to continuously improve
• Comfort combining statistical baselines with ML methods (gradient boosting, GLMs, probabilistic models)

We need a hands‑on lead who can ship useful forecasts and iterate quickly without a big platform team. Energy experience accelerates you, but we’ll value evidence that you can master domain context fast and build pragmatic, explainable models that survive contact with production.

• GB energy markets (half‑hourly settlement, weather‑driven load, network tariffs) and portfolio risk analytics
• Experience with AWS and infrastructure (eg Terraform)
• Experience and willingness to surface insights in relevant UIs/products
• Forecast explainability, uncertainty quantification and decision tooling for non‑technical users
• Optimisation (LP/MIP) for hedging or tariff design

• Curious, pragmatic and outcome‑driven.
• You love shipping models that make decisions better today rather than making them better tomorrow.
• You are energised by early-stage businesses, are adaptable and thrive within startup environments.
• You have a strong bias toward action and having an impact quickly. You want to get sh't done.
• You are a self-starter and autonomous in your way of working, but ultimately believe a team can get further together than any individual.
• You communicate clearly and keep things as simple as possible.

• A dynamic and fast-changing environment with incredible responsibility and autonomy
• Working directly with and learning from experienced startup founders who’ve invested in and scaled businesses up to several billion dollars in valuation
• The ability to build and own something from the very beginning
• Working on market-leading and genuinely impactful products that help solve the climate crisis
• A kick‑ass team of colleagues
• We value in person time at our office in London but offer hybrid/flexible working
• Competitive compensation based on candidate profile with generous employee equity

Apply via LinkedIn or send a short note (with your LinkedIn or CV) to