Geospatial Data Science Engineer

Unikie; Szczecin; On-site; PLN 180,000 - 340,000; Full time

Job summary

Unikie seeks a Geospatial Data Science Engineer to pioneer the data layer powering our next-generation software-defined electric vehicles. In this senior role, you will transform massive, high-frequency streams of vehicle sensor observations into dynamic map features for localization, routing, and ADAS.

You will apply advanced computational statistics, machine learning, and spatial analysis to process large datasets, turning raw fleet telemetry into live, high-definition maps.

Qualifications

• Bachelor's degree in Statistics, Applied Mathematics, Computer Science, Geoinformatics, Robotics, or related quantitative field with emphasis on computational statistics, data mining, ML or spatial optimization.
• 8+ years of professional experience building and maintaining large-scale data processing and predictive systems.
• Expert-level knowledge in data mining and analytic methods (regression, classifiers, clustering, association rules, decision trees, Bayesian networks).
• Proven geospatial experience with geospatial data, noisy time-series datasets, PostGIS, and spatial libraries/indexing systems (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.

Responsibilities

• Geospatial pipeline engineering: design, build, and optimize scalable pipelines ingesting billions of daily observations into map features.
• Statistical modeling & insights: apply data mining and analytics to detect real-world changes and validate map accuracy.
• Map generation & map matching: develop algorithms for map matching, trajectory smoothing, and sensor fusion.
• System design & architecture: lead design of spatial data systems with low-latency queries and scalable storage.
• Stakeholder collaboration: partner with embedded software, perception, and cloud teams to align onboard capabilities with mapping platforms.
• Stay ahead of curve: keep up-to-date with big data, statistics, ML and automated mapping practices.

Skills

Advanced analytics

Geospatial data processing

Statistical testing

Programming & scripting

Education

Bachelor's Degree in Statistics, Applied Mathematics, Computer Science, Geoinformatics, Robotics or related quantitative field

Tools

PostGIS

H3 / S2 / GeoPandas

Python

SQL

Shell scripting

Job 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.
• 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).