Data Scientist – Peer‑to‑Peer Renewable Energy Trading Platform

Data Scientist – Peer‑to‑Peer Renewable Energy Trading Platform

Location: London (Hybrid)

Employment Type: Full‑time, Permanent

About the Company

We are a rapidly scaling peer‑to‑peer Power Purchase Agreement (PPA) platform enabling businesses, generators, and communities to buy and sell renewable electricity directly. Our purpose is to accelerate the shift to a decentralised, transparent, and data‑driven energy system.

We build intelligent systems that optimise matching, pricing, and forecasting across distributed generation and consumption. As we expand across the UK and Europe, we are adding strong analytical and modelling capability to our team.

Role Overview

We are hiring a Data Scientist to develop the forecasting, optimisation, and analytical models that underpin our trading platform. You will design, build, and deploy production‑grade models using energy‑market data, asset telemetry, weather data, settlement data, and commercial datasets.

This role is central to the evolution of our pricing, risk, trading, and optimisation engines. You will collaborate closely with engineering, product, and commercial teams and work with a high degree of autonomy.

Responsibilities

Modelling and Forecasting

• Develop time‑series models for generation, consumption, and market price forecasting.
• Build probabilistic and scenario‑based forecasting capabilities.
• Apply machine learning to optimise matching, pairing, and routing algorithms within the P2P marketplace.

Trading and Optimisation Intelligence

• Create algorithms that optimise buyer–seller matching, pricing, and load balancing.
• Support automated PPA structuring, risk scoring, and exposure modelling.
• Develop data‑driven insights to improve trading efficiency and platform performance.

Data Infrastructure and Engineering

• Work with engineers to design and maintain pipelines for market data, weather feeds, asset data, and settlement information.
• Implement scalable analytics environments and deploy models into production.

Product and Cross‑Functional Collaboration

• Translate modelling outputs into dashboards, APIs, scoring engines, and product features.
• Provide input into product strategy based on model performance and market trends.

Market and Commercial Analytics

• Analyse energy market signals, PPA structures, pricing models, and regulatory factors.
• Develop intelligence around imbalance exposure, generation patterns, demand profiles, and commercial optimisation.

Required Skills and Experience

Technical Skills

• Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‑learn, PyTorch/TensorFlow).
• Strong experience with time‑series modelling (ARIMA, Prophet, LSTMs or similar).
• Understanding of optimisation methods (linear, mixed‑integer, reinforcement learning desirable).
• Strong SQL and practical experience with production‑ready data pipelines.
• Experience working with cloud environments (AWS, GCP, or Azure).

Energy and Market Experience (Highly Desirable)

• Understanding of electricity markets, PPAs, forecasting, imbalance settlement, or asset telemetry.
• Experience with data sources such as system operator data, market pricing feeds, or weather‑driven asset forecasting.

Professional Skills

• Ability to work in a fast‑paced startup environment with autonomy and ambiguity.
• Strong communication and problem‑solving skills.
• Ability to convert complex analytical outputs into actionable business recommendations.

Nice to Have

• Experience with marketplace or matching algorithms.
• Exposure to flexibility markets, virtual power plants, or reconciliation/settlement processes.
• Experience with ML deployment frameworks (MLflow, Vertex AI, SageMaker).
• Knowledge of optimisation libraries such as Gurobi, OR‑Tools, or Pyomo.