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TomTom
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Applied Scientist Intern

TomTom · Madrid, MD, ES

Actively hiring Posted 5 days ago
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Role overview

The Applied Scientist Intern in the MAPS POIs team contributes to the research, experimentation, and development of data-driven and machine-learning solutions that enhance the accuracy, coverage, and usability of TomTom's maps and Points of Interest products. This internship gives you hands-on experience applying scientific and analytical methods to real-world problems at scale, working alongside Applied Scientists and Engineers on challenges that directly impact TomTom's products.

Responsibilities

  • Explore and experiment with ML/AI approaches to solve POI-domain problems such as entity matching, address parsing, data quality assessment, or coverage analysis
  • Implement and evaluate models and algorithmic solutions on real-world, large-scale geospatial datasets
  • Design and run experiments, analyze results, and translate findings into clear insights, recommendations and implementation
  • Be part of the development of data pipelines and tooling that support model training, evaluation, and analysis
  • Collaborate with Applied Scientists, Engineers, and Product stakeholders to understand requirements and integrate your work into the broader team workflow
  • Document experiments, methodologies, and results clearly to support knowledge sharing within the team

Basic qualifications

  • Currently enrolled in a Master's programme in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field
  • Solid grounding in machine learning fundamentals — supervised/unsupervised learning, model evaluation, feature engineering
  • Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn (from coursework, research, or personal projects)
  • Programming proficiency in Python; experience with data manipulation libraries (pandas, NumPy, Spark is a plus)
  • Familiarity with NLP or embedding-based methods (e.g., Sentence Transformers, BERT-based models) is a strong plus
  • Interest in geospatial data, POI systems, addressing, or location intelligence
  • Analytical mindset with the ability to design experiments, interpret results critically, and communicate findings clearly
  • Collaborative and curious — comfortable asking questions, working iteratively, and learning from feedback

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