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# Data Scientist

- **Company:** Comunidad de Madrid
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Continuous Integration, Information Engineering, Software Debugging, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Signal Processing, Software Deployment, Software Engineering, Model Validation, Containerization, Information Technology, Data Management, Machine Learning Operations
- **Published:** August 8, 2026
- **Apply:** https://www.jobleads.com/es/job/eff34154a7fb0c84a399a6a074ddabf73

## About the Role

* Degree in Computer Science, Machine Learning, Statistics, or a related field, or equivalent practical experience.
 * 3-5 years of hands-on experience developing, deploying, and improving ML models in production.
 * Strong production Python skills, with experience turning research code and notebooks into reliable, tested, and observable software for live customer environments.
 * Solid understanding of ML algorithms and statistical modelling, with practical experience using machine learning and deep learning frameworks.
 * Working knowledge of MLOps practices, including model tracking, containerisation, testing, and CI/CD.
 * Practical experience using AI-assisted development tools or agentic workflows for software development, research, or ML experimentation.

Nice to Have

 * Experience with big data tools or data engineering workflows.
 * Familiarity with electricity fundamentals and signal processing applied to energy data.

## Description

We're looking for a hands-on Data Scientist to join our Machine Learning team. You'll build and improve the models that power our energy disaggregation product, working across the full lifecycle from research and prototyping to production deployment., * Develop and improve ML models for energy disaggregation and related use cases.

 * Take ML models and capabilities from research and prototyping through to production deployment, working closely with data engineers and software developers.
 * Help define and improve the KPIs and validation frameworks used to evaluate model performance.
 * Monitor deployed models, troubleshoot performance issues, and iterate based on real-world results.
 * Collaborate with product managers to shape the roadmap for ML initiatives.
 * Use AI-assisted development tools and agentic workflows to support research, coding, debugging, experiment design, and model evaluation.
 * Design iterative ML experimentation loops to test hypotheses, compare results, analyse errors, and move validated improvements into production.


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