Deep time series experience — you understand why random CV splits fail for forecasting, how to handle multiple horizons, and the pitfalls of lookahead bias
Strong feature engineering and selection skills — you've worked with high-dimensional feature sets and know multiple approaches to reduce them systematically
Gradient boosting expertise — XGBoost, LightGBM, or CatBoost are your core tools; you understand their hyperparameters and when each matters
Probabilistic forecasting ability — you can produce calibrated prediction intervals or quantile forecasts, not just point predictions
Rigorous validation mindset — you're paranoid about leakage, you test your assumptions, and you don't trust results that seem too good
Python fluency — clean, testable code; comfortable with pandas/Polars, scikit-learn, and the GBM libraries
SQL competence — you can pull and reshape data from PostgreSQL without friction
Clear communication — you document your work and can explain model behaviour to non-ML colleagues
Experience with MLflow, Hydra, Metaflow, or similar tooling for experiment tracking and pipeline management
Polars experience (we're migrating some workloads from pandas)
Background in energy, utilities, trading, or other domains with similar forecasting challenges
Familiarity with UK energy markets, Elexon data, or grid balancing
Experience with conformal prediction or other modern uncertainty quantification methods
Highly Desirable — Agentic AI Coding Experience
We value candidates who can build software using agentic AI coding systems. This is fundamentally different from using code completion tools or chat-based assistants.
What we're NOT looking for: - GitHub Copilot (code completion/autocomplete) - ChatGPT or similar chat interfaces for generating isolated code snippets - Any tool that only provides single-turn question/answer interactions
What we ARE looking for: Hands-on experience with agentic coding systems such as Claude Code, Codex (OpenAI's agentic coding tool), Open Code, or Cursor.
Ideal candidates will demonstrate:
Breadth of experience — proficiency with at least 2 agentic systems (experience with only one is insufficient)
End-to-end development — ability to design and build software from the ground up using these tools, not just generating isolated snippets
Multi-agent orchestration — demonstrated experience orchestrating multiple agents using skills, tools, and agent coordination, not just one-shot problem solving
Deep system knowledge — familiarity with hooks, permission systems, MCP (Model Context Protocol) servers, custom skills and tool definitions, and context management

Benefits

Plenty of opportunities for learning and professional growth
B2b contract with a paid vacation

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

time series analysisfeature engineeringgradient boostingXGBoostLightGBMCatBoostprobabilistic forecastingPythonSQLconformal prediction

Soft Skills

clear communicationrigorous validation mindset