Applied Scientist Intern

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.

What you'll do:

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

What you'll need:

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

What you'll learn:

Worked on production-scale geospatial and POI data with real business impact

Gained experience in the full ML experimentation cycle - from problem framing and data analysis to model development and evaluation

Deepened your understanding of applied ML in a domain where data quality, scale, and semantic complexity are central challenges

Collaborated in a cross-functional team of scientists, engineers, and product managers