Staff Geospatial Data Science Engineer

About the role

RV Tech 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).

Responsibilities

• Design, build, and optimize scalable data pipelines that ingest, clean, and segment billions of daily vehicle sensor observations into consumable map features and geometries.
• 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.
• 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.
• Partner with embedded software engineers, perception teams, and cloud architects to align onboard vehicle capabilities with cloud-based mapping platforms.
• Stay up-to-date with the latest advancements in big data technologies, computational statistics, machine learning, and automated mapping practices.

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.