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Geoscience Machine Learning Jobs (NOW HIRING)

Terra AI

Senior Geologist

Redwood City, CA · On-site

$185K - $250K/yr

By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise, Terra AI helps exploration and mining companies make faster, more informed subsurface decisions with ...

Terra AI

Senior Geologist

Redwood City, CA · On-site

$185K - $250K/yr

By combining advanced machine learning, probabilistic modeling, and deep geoscience expertise, Terra AI helps exploration and mining companies make faster, more informed subsurface decisions with ...

NobleAI

Senior Applied AI/ML Scientist

Houston, TX · On-site +1

Ph.D. in Geoscience, Geophysics, Production Engineering, Reservoir Engineering, or other Energy-related quantitative discipline * Degree or coursework in Machine Learning * Hands-on experience ...

NobleAI

Senior Applied AI/ML Scientist

Houston, TX · On-site

Ph.D. in Geoscience, Geophysics, Production Engineering, Reservoir Engineering, or other Energy-related quantitative discipline * Degree or coursework in Machine Learning * Hands-on experience ...

NobleAI

Senior Applied AI/ML Scientist

Houston, TX · Remote

Ph.D. in Geoscience, Geophysics, Production Engineering, Reservoir Engineering, or other Energy-related quantitative discipline * Degree or coursework in Machine Learning * Hands-on experience ...

ExxonMobil

Computational Scientist

Spring, TX · On-site

Required : • PhD from a recognized university in Engineering, Applied Mathematics, Geoscience ... learning, machine learning, physics-informed machine learning, reduced-order modeling, multi ...

Viridien

Head of Applied AI Products

Houston, TX · On-site

... machine learning models in collaboration with subject matter experts. • Build enhanced RAG ... Company : Viridien is a leader in cutting-edge geoscience. Founded in 1931, the company is ...

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All JobsGeoscience Machine Learning Jobs

Geoscience Machine Learning information

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$25.5K

$42.6K

$88K

How much do geoscience machine learning jobs pay per year?

As of Jun 7, 2026, the average yearly pay for geoscience machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Geoscience Machine Learning Specialist, and why are they important?

To thrive as a Geoscience Machine Learning Specialist, you need a solid background in geosciences, data analysis, and machine learning, often supported by an advanced degree in geology, geophysics, or computer science. Proficiency with programming languages (such as Python or R), machine learning frameworks (like TensorFlow or Scikit-learn), and GIS or remote sensing tools is typically required. Strong problem-solving skills, collaboration, and effective communication are valuable soft skills for integrating data-driven insights with domain expertise. These competencies enable the development of accurate predictive models and innovative solutions to complex geoscientific challenges.

What is the difference between Geoscience Machine Learning vs Geoscientist?

AspectGeoscience Machine LearningGeoscientist
Required credentialsBackground in geoscience, programming, and data scienceDegree in geology, geophysics, or related field
Work environmentData analysis, modeling, and algorithm development in research or tech firmsFieldwork, laboratory, and research settings
Industry usageApplied in mineral exploration, seismic data interpretation, and environmental modelingConducts field surveys, sample analysis, and geological assessments

While both roles involve geoscience expertise, Geoscience Machine Learning focuses on applying data science and machine learning techniques to geoscientific problems, often in tech-driven environments. Geoscientists typically perform fieldwork and traditional research. The roles complement each other in modern geoscience projects.

What is geoscience machine learning?

Geoscience machine learning is the application of machine learning techniques to analyze and interpret data related to Earth sciences, such as geology, geophysics, meteorology, and environmental science. Professionals in this field use algorithms and statistical models to identify patterns, make predictions, and extract meaningful insights from complex geoscientific datasets. This helps improve understanding of natural phenomena, supports resource exploration, and enhances environmental monitoring. Geoscience machine learning is increasingly important as data volumes grow and traditional analysis methods become less effective for large datasets.

What are some common challenges faced by professionals working in Geoscience Machine Learning roles?

Professionals in Geoscience Machine Learning often encounter challenges such as dealing with sparse, noisy, or incomplete data, especially when integrating geological and geophysical datasets. They must also navigate the complexity of domain-specific knowledge, requiring collaboration with geologists and geophysicists to ensure model validity. Additionally, scaling machine learning models to handle large volumes of spatial or temporal data can be technically demanding. Effective communication and interdisciplinary teamwork are crucial for translating model outputs into actionable insights for exploration, environmental management, or hazard assessment.
Infographic showing various Geoscience Machine Learning job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Corporate Development Geologist / Data Scientist

Expand Energy

Spring, TX

Other

Posted 16 days ago

Job description

Our core values - Stewardship, Character, Collaborate, Learn, Disrupt - are the lens through which we evaluate every business decision. As a dynamic, growing company that offers extremely competitive compensation and benefits, our employees are our most valued assets and the foundation of Expand's performance among our E&P competitors.

We seek applicants from all backgrounds to ensure we get the best, most creative talent on our team. We realize that, historically, underrepresented groups feel the need to be 100% qualified in order to apply. If you meet any combination of our requirements, we encourage you to apply. We strive to hire people from a wide variety of backgrounds, not just because it's the right thing to do, but because it makes our company stronger.

Job Summary

The Corporate Development Geologist plays a critical role in evaluating and advancing the company's strategic growth initiatives by integrating geoscience expertise with advanced data analytics. This position supports mergers and acquisitions (M&A), asset evaluations, portfolio optimization, and newventure screening across the energy value chain. The ideal candidate brings strong subsurface interpretation skills combined with the ability to leverage modern data science tools-including Python, machine learning workflows, and largescale geospatial analysis-to generate actionable insights for investment decisions.

Job Duties & Responsibilities
  • Perform geological assessments of prospective acquisitions, divestitures, and joint ventures across conventional, unconventional, and emerging resource plays
  • Develop basin and playlevel evaluations to support longterm corporate strategy and target screening
  • Build geological risk assessments and volumetric estimates to support valuations and economic modeling
  • Provide technical inputs for M&A financial models, including production forecasts, decline curves, type curves, reserves, and valueimpact drivers
  • Collaborate with Corporate Development, Finance, Reservoir Engineering, and Data Science teams to synthesize geological insights into investment recommendations
  • Participate in diligence processes, including technical deep dives, data room reviews, and management team discussions
  • Prepare executivelevel presentations summarizing the geologic rationale and value proposition of strategic opportunities
  • Interpret and integrate subsurface datasets (well logs, cores, seismic, production data, geochemistry) to assess reservoir quality, productivity, and uncertainty
  • Use Python, SQL, and analytics libraries (e.g., pandas, NumPy, SciPy, scikitlearn) to automate workflows, analyze datasets, and build predictive models
  • Apply machine learning techniques to streamline technical and financial valuations
  • Build tools and dashboards using Power BI, Spotfire, or similar platforms to visualize geological, engineering, commercial and public datasets
  • Integrate large geospatial datasets (GIS layers, satellite imagery, digital elevation models) to support valuation and regional assessments
  • Partner with engineering and operations to evaluate development scenarios, spacing/stacking potential, and reservoir performance
  • Contribute to strategy initiatives such as CCUS site screening, geothermal feasibility, critical minerals potential, or energy transition subsurface evaluations
Job Specific Skills
  • Strong technical proficiency with geological software (e.g., Petrel, Kingdom, ArcGIS, Petra)
  • Handson experience with Python for data analysis, automation, or modeling
  • Familiarity with statistical analysis, uncertainty quantification, and machine learning workflows
  • Ability to integrate geoscience insights with economic and commercial perspectives
  • Strong communication skills with experience presenting to executives or nontechnical audiences
  • Ability to work across functions in a fastpace, dealoriented environment
  • Experience supporting M&A, corporate development, or asset A&D evaluations
  • Knowledge of SQL, cloud environments (Azure, AWS), and modern data pipelines
  • Experience building dashboards (Power BI, Spotfire, Tableau)
  • Background in unconventional reservoir evaluation, CCUS, geothermal, or emerging energy systems
  • Familiarity with decline curve analysis, type curve generation, and integration of geologic inputs into financial models
Education

Minimum: Bachelor's degree - from accredited university - Geology, Geophysics, Petrophysics, Earth Science or related field 

Preferred: Master's degree - from accredited university - Geology, Geophysics, Petrophysics, Earth Science or related field 

Experience

Minimum: 

  • 8-12 years of experience in corporate development, exploration, reservoir characterization, or subsurface evaluation

Expand Energy takes necessary action to ensure that all applicants are treated without regard to their race, color, religion, sex, sexual orientation, age, gender identity, national origin, genetic information, disability, pregnancy, military or veteran status or any other protected characteristic as established by law.

Expand Energy Corporation's operations are focused on discovering and developing its large and geographically diverse resource base of unconventional oil and natural gas assets onshore in the United States.

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