Geospatial Data Science Engineer (6-Month Contract)

About the role

Unikie is looking for a Geospatial Data Science Engineer to build the data layer behind its next-generation software-defined electric vehicles. You will turn massive, high-frequency streams of vehicle sensor observations into accurate, dynamic map features used for localization, routing and ADAS. This is a senior role on a six-month contract, with possible extension.

Key responsibilities

• Design, build and optimise scalable pipelines that ingest, clean and segment billions of daily sensor observations into map features and geometries.
• Apply data mining and analytics to detect changes such as road closures, new lanes and construction, and design statistical tests to validate map accuracy.
• Develop algorithms for map matching, trajectory smoothing and sensor fusion to turn noisy probe data into high-fidelity road geometry.
• Lead the technical design of spatial data systems, with low-latency queries, efficient spatial indexing (H3, S2) and scalable storage of vector and raster map data.
• Work with embedded software engineers, perception teams and cloud architects to align onboard capabilities with cloud-based mapping platforms.
• Stay current with big data, computational statistics, machine learning and automated mapping.

Required profile

• Bachelor's degree in Statistics, Applied Mathematics, Computer Science, Geoinformatics, Robotics or a related quantitative field, with work on computational statistics, data mining, machine learning or spatial optimisation.
• 8+ years of experience building and maintaining large-scale data processing and predictive systems.
• Strong statistical foundation and proven ability to design, run and analyse complex data tests.
• Proven experience with geospatial data, noisy time-series datasets, PostGIS and spatial libraries/indexing systems (H3, S2, GeoPandas).

Required skills

• Data mining and analytics: regression, classifiers, clustering, association rules, decision trees, Bayesian networks.
• Python, SQL and shell scripting (familiarity with R or MATLAB).
• Preferred: crowdsourced map generation, cloud deployment (AWS, GCP, Databricks), open-source tools (Martin, QGIS, GDAL/OGR, GEOS), automotive map standards (NDS, OSM, Lanelet2/HD Maps) and robotics concepts (SLAM, sensor fusion, point clouds).