Data Scientist, Geospatial

present of AI; Houston (TX); Vor Ort; USD 110.000 - 160.000; Vollzeit

Zusammenfassung

present of AI seeks a data scientist to build predictive risk models from messy geospatial data such as LiDAR, terrain and weather. You will turn results into clear, defendable guidance for utility customers and work on-site with the customer team.

You will collaborate across teams, mentor junior staff, and own parts of the data workflow, including spatial pipelines and model validation, to improve decision-making in grid operations.

Qualifikationen

• Hands-on data science or ML experience over several years.
• Experience with spatial data pipelines and coordinate systems.
• Ability to explain findings to senior stakeholders and customers.
• Willingness to collaborate on-site with customers and mentor others.

Aufgaben

• Build predictive risk models from messy geospatial data (LiDAR, GIS, weather).
• Translate model outputs into actionable advice for a utility customer.
• Work closely with the customer on site and explain findings to non-technical audiences.
• Mentor teammates and promote collaboration and ownership.

Kenntnisse

Data science

Machine learning

Spatial data pipelines

Coordinate systems

Stakeholder communication

Collaboration

Tools

LiDAR data

GIS

Python

Model validation

Jobbeschreibung

The role asks for someone who can build predictive risk models from messy geospatial data like LiDAR, terrain and weather, and turn them into clear advice for a utility customer. A strong candidate brings several years of hands on data science or machine learning work, comfort with spatial data pipelines and coordinate systems, and the ability to explain findings to senior people and customer teams. The job also involves working closely with the customer on site and mentoring others, so collaboration and ownership matter as much as technical skill.

How it fits the AI era

AI does the heavy lifting of classifying and modelling large spatial datasets, so the work shifts toward judging whether models reflect real physics and real grid conditions. The human part is framing the right questions, checking data quality, and translating risk numbers into decisions a utility can defend. This role sits between the model and the customer, which keeps communication and domain judgement central.

How it builds AI experience

The role offers hands on practice building physics backed models on a digital twin platform, working with LiDAR, GIS and weather data at network scale. Someone could build this experience by getting comfortable with spatial data pipelines, coordinate transformations and model validation on real infrastructure datasets. Exposure to utility or grid data would help, though the posting treats it as a plus rather than a requirement.

Worth showing

• LiDAR and GIS handling
• Explaining findings to customers

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Neara

A physics-based 3D digital twin of the electricity network, so utilities can simulate a storm or a fire before it happens rather than inspect poles after.