Skill needed: TensorFlow, PyTorch, Python, Machine Learning.
Employer: TomTom
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 analysisImplement and evaluate models and algorithmic solutions on real-world, large-scale geospatial datasetsDesign and run experiments, analyze results, and translate findings into clear insights, recommendations and implementationBe part of the development of data pipelines and tooling that support model training, evaluation, and analysisCollaborate with Applied Scientists, Engineers, and Product stakeholders to understand requirements and integrate your work into the broader team workflowDocument experiments, methodologies, and results clearly to support knowledge sharing within the teamWhat you'll need:Currently enrolled in a Master's programme in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related fieldSolid grounding in machine learning fundamentals — supervised/unsupervised learning, model evaluation, feature engineeringHands-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 plusInterest in geospatial data, POI systems, addressing, or location intelligenceAnalytical mindset with the ability to design experiments, interpret results critically, and communicate findings clearlyCollaborative and curious — comfortable asking questions, working iteratively, and learning from feedbackWhat you'll learn:Worked on production-scale geospatial and POI data with real business impactGained experience in the full ML experimentation cycle - from problem framing and data analysis to model development and evaluationDeepened your understanding of applied ML in a domain where data quality, scale, and semantic complexity are central challengesCollaborated in a cross-functional team of scientists, engineers, and product managers