Neara Data Scientist, Geospatial at Neara building physics-backed models using complex geospatial datasets to prevent wildfires and predict grid failures. Collaborate with utility teams to integrate systems and solve spatial challenges.
Responsibilities
Embed and collaborate directly with customer geospatial and GIS teams in a hybrid setting, spending time on-site at their offices to deeply understand their data workflows, integrate systems, and solve complex spatial challenges.
Build predictive risk models on Neara that combine LiDAR, terrain models, and weather data to simulate conductor sag, sway, and vegetation fall-in risks to directly prevent wildfire ignitions.
Translate complex spatial and structural risk metrics into defensible, data-driven insights that inform utility capital investment decisions for grid hardening.
Build and maintain robust spatial data pipelines to ingest, clean, vectorize, and integrate diverse geospatial data formats from our utility partner.
Establish QA/QC benchmarks for spatial accuracy, coordinate reference system (CRS) transformations, and structural alignment across disparate data sources.
Mentor others in best practices for model training, data analytics, and building data-driven products.
Qualification
Strong quantitative intuition for dataExperience working in fast-pacedSelf-driven with a bias for actionGroundedGeospatial data experiencePower grid domain experience
Required
5+ years of hands-on experience in data science or machine learning. Geospatial data or power grid domain experience is a plus.
Strong quantitative intuition for data, with excellent communication skills and proven experience presenting complex findings to customers and senior leaders.
Experience working in fast-paced, high-growth scale-up technology environments is a plus.
Self-driven with a bias for action; comfortable taking full ownership of complex projects and adapting quickly to shifting priorities.
Natural collaborator who thrives at the intersection of technical teams (ML, Software) and external customer stakeholders.
Grounded, resourceful approach to problem-solving, with a track record of navigating messy, real-world datasets to deliver clear, business-driven outcomes.
