- Develop multi-horizon time series models for UK energy balancing markets
- Build gradient boosting models (XGBoost, LightGBM, CatBoost) with a strong focus on probabilistic outputs (quantiles, prediction intervals, distributions)
- Design robust time-series cross-validation schemes that respect temporal structure
- Engineer features from large, messy market datasets (prices, volumes, grid conditions, weather, calendar effects)
- Work with hundreds of thousands of candidate features using systematic selection methods (mRMR, importance-based selection, recursive elimination)
- Analyse feature stability across time periods and market regimes
- Apply domain reasoning to understand why a feature improves performance — and whether that improvement is real
- Actively hunt for and eliminate target leakage
- Validate preprocessing steps to ensure no look-ahead bias
- Use synthetic or controlled data to test pipeline assumptions
- Build confidence that models will generalise, not just interpolate
- Track experiments using MLflow or similar tooling
- Maintain reproducible training pipelines
- Clearly document model choices, assumptions, and validation results
- 4–7+ years building production-quality ML models with a deep focus on time series forecasting
- Strong experience with high-dimensional feature engineering and selection
- Expert-level use of gradient boosting for tabular data
- Proven experience delivering probabilistic forecasts, not just point predictions
- A rigorous validation mindset — you're sceptical of results that look "too good"
- Strong Python (pandas/Polars, scikit-learn, GBM libraries)
- Confident SQL (PostgreSQL or similar)
- Clear communicator who documents decisions and trade-offs
- Experience in energy, utilities, trading, or similar market-driven forecasting domains
- Familiarity with UK energy market data or balancing mechanisms
- Polars experience
- Conformal prediction or modern uncertainty quantification techniques
- Used multiple agentic systems (e.g. Claude Code, Codex, Cursor, Open Code)
- Built software end-to-end using these tools
- Orchestrated multiple agents and worked with permissions, tools, context management, or MCP servers
- Fully remote, async-friendly environment
- At least 4 hours daily overlap with UK business hours
- Competitive compensation depending on location and experience
- Up to 300 Euros per day
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Senior Data Scientist - EMEA - SR2 | Socially Responsible Recruitment | Certified B Corporation™

2 hari yang lalu
Deskripsi
Senior Data Scientist – Time Series Forecasting (Energy Markets)
Remote (UK / EU time zones)
We're supporting a well-funded energy technology business working at the sharp end of the UK energy balancing markets. They're looking for a Senior Data Scientist who genuinely specialises in time series forecasting, not a generalist who's dabbled in it.
This role is about craft: building forecasting models that actually hold up in production, not just models that look good on a backtest.
What you'll be doing
Building serious forecasting models
Feature engineering at scale
Rigorous validation
Reproducibility & documentation
You won't own deployment, but you will be expected to hand over well-documented, production-ready models to an experienced DevOps team.
What we're looking for
Essential
Nice to have
Highly desirable: agentic AI coding experience
This team values hands-on experience with agentic coding systems, not basic code completion tools.
They're specifically interested in candidates who have:
This is not a role for people whose experience is limited to Copilot-style autocomplete or one-shot chat prompts.
Practicalities
If you're a time-series specialist who enjoys digging into messy data, challenging assumptions, and building models that survive real-world scrutiny, this is a rare opportunity.
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