Pay
$118K–$194K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,757 postings in the last year. Stated ranges, not salaries paid.
Houston · Houston, Texas, United States
Likely evergreen, first seen by us 12 days agoLocation type: On-siteFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: Customer Success › Customer Success - USFrom the employer’s posting
Will you now or in the future require sponsorship for an employment visa status (e.g., H-1B, L-1, etc.)?From the employer’s application form
Build predictive risk models simulating conductor sag, sway and vegetation fall-in
Translate spatial and structural risk metrics into insights for capital investment decisions
Build and maintain spatial data pipelines to ingest, clean and vectorize utility data
Set QA/QC benchmarks for spatial accuracy and coordinate system transformations
Embed with customer GIS teams on-site to understand data workflows and solve spatial problems
Build predictive risk models on Neara that combine LiDAR, terrain models, and weather data to simulate conductor sag, sway, and vegetation fall-in risks
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
Embed and collaborate directly with customer geospatial and GIS teams in a hybrid setting, spending time on-site at their offices
5+ years of hands-on experience in data science or machine learning. Geospatial data or power grid domain experience is a plus.
Experience working in fast-paced, high-growth scale-up technology environments is a plus.
Likely evergreenReposted 2 times since Sep 22, 2026. A role that keeps coming back is often kept open to collect applications rather than to fill one seat.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
$118K–$194K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 3,757 postings in the last year. Stated ranges, not salaries paid.
Day 12
Similar jobs usually come down between day 7 and day 57.
From 10,595 similar postings that came down. Coming down is not always a hire.
6 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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Yes. We’ve followed 2 other Data Scientist, Geospatial jobs there since September 2026. 2 came down, usually after about 16 days.
New Data Scientist postings at the employers we follow are down 3% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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.
#LI-NT1 #LI-DNP
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Houston, Texas, United States
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.
No clear work-authorization passage found. Eligibility is unconfirmed.