ppThe 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. /p h3What you'll do: /h3 ul liExplore and experiment with ML/AI approaches to solve POI-domain problems such as entity matching, address parsing, data quality assessment, or coverage analysis /li liImplement and evaluate models and algorithmic solutions on real‑world, large-scale geospatial datasets /li liDesign and run experiments, analyze results, and translate findings into clear insights, recommendations and implementation /li liBe part of the development of data pipelines and tooling that support model training, evaluation, and analysis /li liCollaborate with Applied Scientists, Engineers, and Product stakeholders to understand requirements and integrate your work into the broader team workflow /li liDocument experiments, methodologies, and results clearly to support knowledge sharing within the team /li /ul h3What you'll need: /h3 ul liCurrently enrolled in abMaster's programme /bin Computer Science, Data Science, Artificial Intelligence, Machine Learning,



or a related field /li liSolid grounding in machine learning fundamentals — supervised/unsupervised learning, model evaluation, feature engineering /li liHands‑on experience with ML frameworks such as PyTorch, TensorFlow, or scikit‑learn (from coursework, research, or personal projects) /li liProgramming proficiency in Python; experience with data manipulation libraries (pandas, NumPy, Spark is a plus) /li liFamiliarity with NLP or embedding-based methods (e.g., Sentence Transformers, BERT-based models) is a strong plus /li liInterest in geospatial data, POI systems, addressing, or location intelligence /li liAnalytical mindset with the ability to design experiments, interpret results critically, and communicate findings clearly /li liCollaborative and curious — comfortable asking questions, working iteratively, and learning from feedback /li /ul h3What you'll learn: /h3 ul liWorked on production-scale geospatial and POI data with real business impact /li liGained experience in the full ML experimentation cycle - from problem framing and data analysis to model development and evaluation /li liDeepened your understanding of applied ML in a domain where data quality, scale, and semantic complexity are central challenges /li liCollaborated in a cross‑functional team of scientists, engineers, and product managers /li /ul /p #J-18808-Ljbffr

📌 Applied Scientist Intern (Madrid)
🏢 TomTom
📍 Madrid