Geospatial Data Science Engineer
Senior engineer at Unikie who turns billions of daily vehicle sensor observations into dynamic, high-accuracy map features for software-defined electric vehicles, supporting localization, routing, and ADAS. Core stack: Python, SQL/PostGIS, spatial indexing (H3/S2), statistical modeling/ML, and cloud-based geospatial pipelines.
- Compensation
- Not disclosed
- Location
- Sławińska, Poland
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Data science
- Posted
Skills
Must have
- Bachelor's Degree in Statistics, Applied Mathematics, Computer Science, Geoinformatics, Robotics, or related quantitative field
- 8+ years of professional experience building and maintaining large-scale data processing and predictive systems
- Expert-level knowledge of data mining and analytics: regression, classifiers, clustering, association rules, decision trees, Bayesian networks
- Proven geospatial data experience, including noisy time-series handling and advanced spatial queries in PostGIS
- Experience with spatial libraries/indexing systems such as H3, S2, GeoPandas
- Proficiency with Python, SQL, and shell scripting (or familiarity with R/MATLAB)
- Strong statistical foundation with expertise in designing, executing, and analyzing complex data tests and validation methodologies
Nice to have
- Experience with fleet-based, crowdsourced map generation: probe-data aggregation, change detection, incremental map updates
- Experience deploying large-scale geospatial pipelines and ML workloads on cloud platforms (AWS, GCP, Databricks)
- Experience with open-source vector tile formats and tools like Martin, QGIS, GDAL/OGR, GEOS
- Familiarity with automotive map standards and formats such as NDS, OpenStreetMap, Lanelet2/HD Maps
- Familiarity with robotics concepts: SLAM, sensor fusion, point cloud/sensor processing (LiDAR, radar, vision)
Full description
Unikie is seeking a Geospatial Data Science Engineer to pioneer the data layer powering our next-generation software-defined electric vehicles. In this senior role, you will be responsible for transforming massive, high-frequency streams of vehicle sensor observations into highly accurate, dynamic map features that the vehicle can use for localization, routing, and advanced driver assistance systems (ADAS).
You will apply advanced computational statistics, machine learning, and spatial analysis to process and model large datasets, drawing actionable insights that translate raw fleet telemetry into live, high-definition maps. We value proactive problem-solvers who thrive in autonomous roles and are passionate about building performant, scalable systems at the intersection of data science and robotics.
Responsibilities
- Geospatial Pipeline Engineering: Design, build, and optimize scalable data pipelines that ingest, clean, and segment billions of daily vehicle sensor observations into consumable map features and geometries.
- Statistical Modeling & Insights: Apply advanced data mining and analytic methods to detect real-world changes (e.g., road closures, new lanes, construction) and design rigorous statistical tests to validate map accuracy.
- Map Generation & Map Matching: Develop robust algorithms for map matching, trajectory smoothing, and sensor fusion to convert noisy probe data into high-fidelity road geometry and attributes.
- System Design & Architecture: Lead the technical design of spatial data systems, ensuring low-latency query performance, efficient spatial indexing (e.g., H3, S2), and scalable storage of vector/raster map data.
- Stakeholder Collaboration: Partner with embedded software engineers, perception teams, and cloud architects to align onboard vehicle capabilities with cloud-based mapping platforms.
- Staying Ahead of the Curve: Stay up-to-date with the latest advancements in big data technologies, computational statistics, machine learning, and automated mapping practices.
Qualifications
Minimum Qualifications
- Education: Bachelor's Degree in Statistics, Applied Mathematics, Computer Science, Geoinformatics, Robotics, or a related quantitative field with an emphasis or thesis work on computational statistics, data mining, machine learning, or spatial optimization.
- Experience: 8+ years of related professional experience building and maintaining large-scale data processing and predictive systems.
- Advanced Analytics & Data Mining: Expert-level knowledge in data mining and analytic methods such as regression, classifiers, clustering, association rules, decision trees, and Bayesian network analysis.