Sr Data Scientist

HERE Technologies•United States Home Office,

4d•Remote

About The Position

We are looking for a Senior Applied Data Scientist to drive the development of large‑scale computer vision and machine learning solutions that automatically extract high‑fidelity map features from Aerial and Satellite imagery. In this role, you will leverage vision foundation models, deep learning, and geospatial analytics to power HERE’s global mapmaking products. You will work on cutting‑edge problems such as automated extraction of road & lane boundary and buildings, operating at planetary scale with diverse imagery sources and conditions. This position sits at the intersection of AI research and production systems, with direct impact on HERE’s next‑generation maps used by millions worldwide.

Requirements

Master’s or PhD in Computer Science, Machine Learning, Data Science, Geomatics, Remote Sensing, or related field (or equivalent practical experience).
hands‑on experience applying machine learning or computer vision to real‑world production problems.
Strong expertise in deep learning for images, including CNNs, transformers, and large vision models.
Practical experience working with satellite or aerial imagery, geospatial data, or remote sensing datasets.
Proficiency in Python and major ML frameworks (e.g., PyTorch, TensorFlow).
Experience designing experiments, evaluating model quality, and driving continuous improvement in deployed systems.

Nice To Haves

Experience with vision foundation models or large‑scale pre‑trained models for imagery.
Knowledge of geospatial data formats, coordinate systems, and spatial analysis.
Experience processing data at global or near‑global scale.
Familiarity with MLOps, model monitoring, and lifecycle management in production.
Contributions to published research, patents, or open‑source projects in computer vision or GeoAI.

Responsibilities

Design, develop, and deploy computer vision and machine learning models to automatically extract map‑observable features from satellite imagery.
Leverage and adapt vision foundation models and modern deep learning techniques for geospatial feature extraction at global scale.
Build scalable, production‑ready ML pipelines that process large volumes of satellite imagery with high accuracy and efficiency.
Integrate ML outputs into HERE’s mapmaking systems, ensuring alignment with cartographic quality, consistency, and accuracy standards.
Address real‑world geospatial challenges such as variable imagery resolution, diverse sensors, regional differences, and environmental conditions.
Define evaluation metrics, conduct experiments, and drive continuous improvement of model performance and map data quality.
Collaborate cross‑functionally with data engineering, mapping, and product teams to translate business needs into ML solutions.