Imagine having the power to stress-test an entire power grid against a hurricane or thunderstorm before the clouds even gather. That is the reality we are creating at Neara.

We use advanced machine learning to create engineering-grade, physics enabled digital twins of electricity grids across four continents, this helps asset owners understand their biggest challenges and bring the most viable solutions to life across millions of kilometres of infrastructure.

By simulating extreme weather and structural stress at a network-wide scale, we empower the world’s largest utilities to pinpoint risks, optimise investments and build a more resilient global energy future.

Our team is a collection of brilliant minds who are fanatical about making a tangible difference in the real world, utilising AI and machine learning to accelerate everything from data classification to complex scenario analysis. We have built a special culture where innovation thrives because everyone owns the mission and we need smart, creative people to help us scale this impact to every corner of the globe.

Data Scientist, Geospatial

We're looking for a Data Scientist, Geospatial to join our Houston team, supporting a key regional electric utility. You’ll work directly with their team to build physics-backed models on Neara, leveraging complex datasets like LiDAR, GIS, asset records, and weather data. Your work will directly prevent wildfires by simulating vegetation encroachment, predict grid failures under extreme weather loads, and inform capital investment decisions to harden critical infrastructure.

What You Will Do:

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.

Who You Are:

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.

What We Offer:

Work with an industry-leading, multi-modal spatial data stack - including airborne LiDAR, high-resolution satellite imagery, and physics-enabled digital twins - to solve real-world engineering challenges at immense scale.
Your spatial modeling will directly mitigate wildfire risks, optimize utility vegetation management, and harden critical infrastructure across millions of kilometers of energy grids.
Shape the spatial architecture of our core product line while collaborating with top-tier ML and software engineers.
Enjoy a highly competitive compensation package with a significant equity component, ensuring you are a true stakeholder in our rapid global scaling.

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