Applied Scientist Intern
Actively Hiring
Full-time Posted about 2 months ago
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
- check_circle Explore and experiment with ML/AI approaches to solve POI-domain problems such as entity matching, address parsing, data quality assessment, or coverage analysis
- check_circle Implement and evaluate models and algorithmic solutions on real-world, large-scale geospatial datasets
- check_circle Design and run experiments, analyze results, and translate findings into clear insights, recommendations and implementation
- check_circle Be part of the development of data pipelines and tooling that support model training, evaluation, and analysis
- check_circle Collaborate with Applied Scientists, Engineers, and Product stakeholders to understand requirements and integrate your work into the broader team workflow
- check_circle 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
Tags & Focus Areas
Internship Ai