Power Data Scientist

Marlin Selection Recruitment; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

A London-based commodity trading firm seeks a Power Analyst - Data Scientist to develop ML forecasting models for European power markets, working with traders and data teams.

You will productionise time-series models for wind and solar, build forecasting pipelines, onboard data feeds via Databricks, and contribute to trading performance.

Qualifications

• PhD or equivalent in a quantitative discipline (ML/DS/Math).
• Experience deploying production-grade ML models with MLOps, Git and CI/CD.
• Strong Python with Pandas, Scikit-learn, and PyTorch or Keras/TensorFlow.
• Proven time-series forecasting experience, ideally in energy/commodities.
• SQL knowledge; Databricks experience; energy/commodities exposure advantageous.

Responsibilities

• Develop and productionise ML time-series forecasting models for wind/solar.
• Build and maintain forecasting pipelines for traders.
• Collaborate with data engineering to onboard data feeds and monitor quality.
• Analyse power market fundamentals and incorporate features into models.
• Maintain dashboards and reports for analysts and stakeholders.

Skills

Python

Time-series forecasting

MLOps

Education

PhD or equivalent in a quantitative discipline

Tools

Databricks

Git

CI/CD

Pandas

Scikit-learn

PyTorch

Keras/TensorFlow

Job description

Our client, a leading commodity trading firm, is looking for a Power Analyst - Data Scientist to join their team in London. This is an exciting opportunity to develop and enhance machine learning forecasting models that support trading decisions across European power markets. Working closely with traders, analysts and data teams, you will help improve forecasting accuracy and contribute directly to trading performance.

Your responsibilities will include:

• Developing, enhancing and productionising machine learning time-series forecasting models, with an initial focus on wind and solar generation.
• Building, automating and maintaining forecasting pipelines to ensure accurate, reliable and timely model outputs for traders.
• Collaborating with data engineering teams to onboard new data feeds, monitor data quality and continuously improve model performance.
• Analysing power market fundamentals and incorporating new features into forecasting models as renewable generation evolves.
• Maintaining dashboards and reports while providing technical modelling support to analysts and key stakeholders.

You will need to have the following:

• A PhD or equivalent advanced qualification in Machine Learning, Data Science, Statistics, Mathematics or another quantitative discipline.
• Experience building and deploying production-grade machine learning models, with a solid understanding of MLOps, Git and CI/CD practices.
• Strong Python programming skills, including experience with Pandas, Scikit-learn and either PyTorch or Keras/TensorFlow.
• Proven experience developing time-series forecasting models, ideally within energy, commodities, financial markets or another data-intensive environment.
• Knowledge of SQL is essential, with experience of Databricks and exposure to energy or commodity trading considered advantageous.