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Neara is building physics-enabled digital twins of electricity grids using advanced machine learning to help utilities understand and mitigate infrastructure risks. The company simulates extreme weather and structural stress at network-wide scale to help asset owners pinpoint risks, optimize investments, and build resilient energy systems.
As a Data Scientist, Geospatial, you will embed directly with a key regional electric utility in Houston, working in a hybrid setting with time spent on-site at their offices. You'll build predictive risk models that combine LiDAR, terrain models, and weather data to simulate conductor sag, sway, and vegetation fall-in risks—directly preventing wildfire ignitions and grid failures under extreme weather loads.
Key responsibilities include:
- Collaborating directly with customer geospatial and GIS teams to understand data workflows, integrate systems, and solve complex spatial challenges
- Building predictive risk models on Neara's platform that inform utility capital investment decisions for grid hardening
- Translating complex spatial and structural risk metrics into defensible, data-driven insights for senior leaders and customers
- Building and maintaining robust spatial data pipelines to ingest, clean, vectorize, and integrate diverse geospatial data formats
- Establishing QA/QC benchmarks for spatial accuracy, coordinate reference system (CRS) transformations, and structural alignment across disparate data sources
- Mentoring others in best practices for model training, data analytics, and building data-driven products
You will work with an industry-leading spatial data stack including airborne LiDAR, high-resolution satellite imagery, and physics-enabled digital twins. Your work will directly mitigate wildfire risks, optimize utility vegetation management, and harden critical infrastructure across millions of kilometers of energy grids.
REQUIREMENTS:
- 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 (preferred)
- Self-driven with 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 track record of navigating messy, real-world datasets to deliver clear, business-driven outcomes
