Senior Computational Geophysicist

Zanskar

Salt Lake City, Utah, USA

Posted on: 2026-03-05

Category: energy

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Employment type:

Full time

Experience required:

Senior

Salary

Salary not provided

About the company:

Powering the future with the Earth’s hidden heat.

Geothermal is carbon-free, always-on power but most of the world’s geothermal resources remain undiscovered and undeveloped.

Zanskar is changing that. We take a AI and data-driven, scalable approach to geothermal discovery and development combining modern geoscience and advanced field methods to identify and tap into overlooked or underutilized geothermal resources.

By making geothermal faster and more predictable to develop, we’re unlocking its potential to deliver gigawatts of clean, firm energy and to become a cornerstone of our future energy system.

Senior Computational Geophysicist

(Electromagnetic and/or potential fields methods)

Role Overview

Title: Senior Computational Geophysicist

Hours: Full-Time, Salaried

Location: Salt Lake City, UT, Hybrid (3 days in office, 2 days can be remote)

Benefits Eligible: Yes

Manager: Head of Reservoir R&D

Mission — Why We Exist and Why We Need You

Geothermal energy is the most abundant renewable energy source on Earth—containing ~2,300× more

energy than all fossil fuels combined. Yet despite this enormous potential, geothermal has historically

been difficult, slow, and expensive to discover and develop.

At Zanskar, we are transforming geothermal exploration by combining geophysics, geology, and AI-driven

modeling to dramatically increase discovery rates and reduce development risk. We operate at scale and

at speed, setting new industry standards for how geothermal prospects are identified and de-risked.

To support this mission, we are hiring a Senior Computational Geophysicist with deep, hands-on

experience in electromagnetic and/or potential fields methods who can take real field data from active

geothermal sites and turn it into defensible subsurface interpretations. This role is fundamentally about

streamlining and automating inversion workflows that incorporate structural, lithological, and hydrothermal

constraints: ensuring models reflect geological reality, not just a mathematical best-fit.

Equal Opportunity Employer

Zanskar is an equal-opportunity employer and complies with all applicable federal, state, and local

fair employment practice laws.

Core Responsibilities

Lead and execute stochastic and traditional inversions on active geothermal sites, producing subsurface models that materially de-risk drilling and development decisions

Work extensively with electromagnetic and/or potential fields data, including: heliTEM, magnetotellurics (MT), gravity and gagnetic data

Quantify uncertainty and assess model robustness in decision-critical settings

Integrate inversion results with geological models, structural interpretations, well data, and drilling outcomes

Develop and maintain Python-based tools and pipelines that automate inversion workflows, reduce manual overhead, and enable consistent, repeatable results across many sites

Collaborate closely with geologists and ML researchers

Communicate results clearly, including uncertainties, assumptions, and recommended next steps

Contribute to patentable methods and tools that advance geothermal exploration

Required Experience

Multiple years of hands-on inversion experience on real-world geothermal or subsurface projects, across multiple sites and datasets

Direct experience with EM and/or potential fields methods (heliTEM, MT, gravity, and/or magnetics) from raw field data through to full inversion workflows

Strong programming skills in Python, including experience building reproducible, maintainable scientific software; familiarity with inversion frameworks, numerical optimization, or HPC environments is a strong plus

Experience implementing or modifying inversion algorithms, and comfort with stochastic, Bayesian, and regularization-based approaches

Solid physical intuition for geophysical observables and the ability to reconcile inversion results with geological, structural, and operational constraints

Comfort operating under imperfect data, time pressure, and evolving site understanding

Proven ability to collaborate across disciplines: geologists, field teams, ML researchers, and business leaders, and communicate clearly about what the data supports, what it doesn't, and where uncertainty matters

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