Head of Data Science - Cartography

About the role

The company is looking for a Head of Data Science - Cartography to direct its Data Science function and transform mapping technologies at scale. You will define the Data Science strategy, lead a team of Data Scientists and drive AI/ML innovations that improve map accuracy, freshness, coverage and quality. The role works with large-scale datasets and bridges technical execution, product strategy and people leadership with Product, Engineering and Data Engineering.

Key responsibilities

• Define, execute and scale a Data Science roadmap aligned with business priorities and product vision.
• Drive end-to-end AI/ML solutions from problem definition and experimentation to production deployment, monitoring and optimization.
• Solve spatial challenges such as map matching, geocoding, POI intelligence, road networks, routing and map quality.
• Guide architectural and algorithmic choices in machine learning, statistical modeling, optimization, deep learning and explainable AI.
• Recruit, mentor and develop the Data Science team, including career growth, capability development and succession planning.
• Work with Product, Engineering and Data Engineering to turn business challenges into scalable data products.
• Stay ahead of emerging AI, ML and geospatial technologies and bring them into the Cartography organization.

Required profile

• 10+ years of experience in Data Science, Machine Learning or AI, with a track record of managing and developing Data Science teams.
• Bachelor's or Master's degree in a quantitative discipline (Statistics, Econometrics, Mathematics, Physics, Computer Science or related).
• Demonstrated application of Data Science and ML to geospatial, mapping, navigation, mobility or location intelligence problems.
• Proven ability to handle large-scale spatial, mobility, behavioral, GPS, POI and road network data and build production ML systems.

Required skills

• Expert Python and SQL; computer vision and graph neural networks or graph-based ML.
• Graph data structures and algorithms, and explainable AI.
• Cloud platforms (GCP, AWS, Azure) and orchestration tools (Flink, Airflow).
• Git, CI/CD, testing and production ML workflows.