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# Data Scientist

- **Company:** We ARE Recruitment Group
- **Location:** Rotterdam, Netherlands
- **Contract:** Permanent contract
- **Skills:** Microsoft Azure, Code Review, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Open Source Technology, Backtesting, Large Language Models, Information Technology, Machine Learning Operations, Software Version Control, Databricks
- **Published:** October 11, 2026
- **Apply:** https://www.adzuna.nl/details/5922432623

## About the Role

Completed university degree in data science, econometrics, mathematics, statistics, electrical engineering, computer science, or a similar field.
Demonstrable experience with time-series forecasting and machine learning in a professional environment.
Very good command of Python, including testing, code reviews, and version control.
Experience with backtesting, experiment design, model comparison, and forecast metrics.
Experience with production environments, MLflow/MLOps, and preferably Databricks and Azure.
Familiarity with probabilistic forecasting, calibration, ensembles, or foundation models is a strong advantage.
Experience with open-source development or contributing to a technical community is a plus.
You communicate clearly with both technical colleagues and business stakeholders and document decisions transparently.
You work independently, actively seek collaboration, and demonstrably transfer knowledge.

## Description

Within the Data Science Forecasting team, you will work on reliable short-term forecasts of grid load. The assignment is specifically focused on implementing the OpenSTEF model for all low-voltage transformers. OpenSTEF is the grid operator-wide open-source forecasting package for short-term (<7 days) forecasts of grid load. The client uses these forecasts for daily operations, particularly for capacity management and grid safety analyses. By the end of Q2 2027, the quality of all medium-voltage transformer forecasts (23,000 assets) must be substantially improved (ACM deadline).

Goal and content of the assignment
Fully operational OpenSTEF implementation for the 23,000 low-voltage transformer points with implemented features for quality improvement. Together with data scientists, machine-learning engineers, the Product Owner, and stakeholders, you will determine which improvements yield the greatest forecast gains. You will investigate, implement, and validate these improvements, with explicit attention to production suitability, explainability, and reusability.

 * Analysis of current forecast quality and selection of locations, horizons, and situations with the greatest improvement potential.
 * Backtesting and benchmarking OpenSTEF methods against the existing reference, with appropriate quality metrics and segmentation.
 * Improvements for difficult patterns, such as flatlines, peaks, missing values, unpredictable assets, and limited historical data.
 * Experiments with model selection, ensembles, calibration, and confidence intervals.
 * Exploration and validation of foundation model capabilities once they are applicable and responsibly deployable.
 * Translation of successful experiments into maintainable Python code, tests, configuration, and reproducible pipelines.
 * Improvement of monitoring for forecast quality, data quality, and model behavior.
 * Contributing to OpenSTEF, for example via issues, documentation, reviews, or pull requests, when an improvement is also valuable to the community.
 * Knowledge transfer to the team and clear documentation of choices, results, and next steps.

Skillset/profile:
You can quickly test hypotheses, but also know what is needed to reliably deploy a model in a critical production chain.