Geospatial Data Science Engineer

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. Geospatial Expertise: Proven experience working with geospatial data, handling noisy time-series datasets, writing advanced spatial queries in PostGIS, and utilizing spatial libraries/indexing systems (e.g., H3, S2, GeoPandas). Programming & Scripting: Proficiency with statistical analysis packages and programming languages, including Python, SQL, and shell scripting (or familiarity with R/MATLAB). Statistical Testing: Strong statistical foundation with proven expertise in designing, executing, and analyzing complex data tests and validation methodologies. Preferred Qualifications Crowdsourced Mapping: Experience with fleet-based, crowdsourced map generation methodologies, including probe-data aggregation, change detection, and incremental map updates. Cloud Ecosystems: Experience deploying large-scale geospatial pipelines and machine learning workloads within cloud platforms (e.g., AWS, GCP, Databricks). Open-Source Infrastructure: Experience with standard open-source vector tile formats, tools like Martin, QGIS, and geospatial processing engines (GDAL/OGR, GEOS). Automotive Map Formats: Familiarity with automotive map standards, routing schemas, and data structures such as NDS (Navigation Data Standard), OpenStreetMap (OSM), and Lanelet2/HD Maps. Robotics Algorithms: Familiarity with core robotics concepts, including SLAM (Simultaneous Localization & Mapping), Sensor Fusion, and Point Cloud / Sensor Processing (LiDAR, radar, vision).