The Applied Scientist in the MAPS POIs team contributes to the design, development, and continuous improvement of data‑driven and machine‑learning solutions that enhance the accuracy, coverage, and usability of TomTom’s maps and Points of Interest products.

This role applies scientific and analytical methods to solve complex, real‑world problems at scale, translating data and models into reliable, production‑ready solutions that create value for TomTom customers and internal stakeholders.

What you'll do:
- Lead the design, implementation, and integration of scalable AI/ML systems and services within the POI domain, setting direction for critical technical components
- Own and deliver end‑to‑end AI/ML solutions for large‑scale, high‑impact initiatives, ensuring quality, scalability, and long‑term maintainability
- Solve complex, ambiguous problems, defining approaches where no clear path exists and applying innovative, data‑driven solutions
- Design and develop advanced,



production‑grade algorithmic solutions and ML models that address challenging real‑world use cases at scale
- Shape the technical architecture within your domain, making decisions that influence systems across teams and long‑term platform evolution
- Proactively identify risks, trade‑offs, and technical dependencies, driving alignment and resolution across stakeholders
- Champion best practices and continuously raise the bar on model development, system design, and engineering excellence
- Produce high‑quality, production‑level code and lead by example through thoughtful and impactful code reviews
- Act as a go‑to expert within the team, providing technical leadership and guidance in your area of expertise
- Mentor Applied Scientists (Levels I–III), supporting their growth and strengthening the overall capability of the team
- Collaborate across teams and influence Product Managers, Program Managers, and leadership on AI/ML strategy, solutions, and trade‑offs

What you'll need:
- Strong, up‑to‑date

📌 Senior Applied Scientist (Madrid)
🏢 Tomtom-7dbebcc
📍 Madrid