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Neara

Physics-based digital twin platform for infrastructure

Data Scientist - Geospatial

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeHouston, TX, USA

About the Job

Requirements

  • At least 5 years of hands-on experience in data science or machine learning.
  • Strong quantitative intuition for data.
  • Experience presenting complex findings to customers and senior leaders.
  • Experience working in fast-paced, high-growth scale-up technology environments is a plus.
  • Ability to take ownership of complex projects and adapt to shifting priorities.
  • Ability to collaborate with technical teams and external customer stakeholders.
  • Experience navigating messy, real-world datasets and delivering business-driven outcomes.

Responsibilities

  • Collaborate directly with customer geospatial and GIS teams in a hybrid setting, spending time on-site at their offices to understand data workflows, integrate systems, and solve 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 prevent wildfire ignitions.
  • Translate spatial and structural risk metrics into data-driven insights that inform utility capital investment decisions for grid hardening.
  • Build and maintain spatial data pipelines to ingest, clean, vectorize, and integrate geospatial data formats from the utility partner.
  • Establish quality assurance and quality control benchmarks for spatial accuracy, coordinate reference system transformations, and structural alignment across data sources.
  • Mentor others in best practices for model training, data analytics, and building data-driven products.

Desired Qualifications

  • Geospatial data or power grid domain experience.

About the Company

Neara

Physics-based digital twin platform for infrastructure

Neara builds a SaaS platform that creates a precise 3D digital twin of entire infrastructure networks. It fuses GIS data, LiDAR imagery, and engineering specifications to produce an engineering-grade model of the network. The platform uses physics-based simulations and AI to test asset behavior under real-world scenarios, helping utilities anticipate risks, optimize maintenance, and plan upgrades. It differentiates by applying rigorous, cross-sector physics models at scale for major utilities, with a goal to expand internationally and extend into telecom and public transportation to support grid reliability and the energy transition.

Company size
201-500
Company stage
Series D
Total funding
$114.9M
Headquarters
Sydney NSW, Australia
Founded
2016

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