AI-Enabled Geologist at AI Talent
Sydney NSW 2000, New South Wales, Australia -
Full Time


Start Date

Immediate

Expiry Date

29 Aug, 25

Salary

70000.0

Posted On

29 May, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Geophysics, Mining, Geology, Gas, Training, Data Driven Decision Making, Data Analysis, Soft Skills, Python, R

Industry

Information Technology/IT

Description

AI-ENABLED GEOLOGIST

Hybrid Work $70,000 - $120,000 a year - Permanent, Full-time
Location: Sydney NSW 2000
Benefits: Work from home flexibility

ABOUT US:

We are at the forefront of revolutionising geological analysis and resource exploration through the application of cutting-edge Artificial Intelligence. Our mission is to leverage AI to enhance data interpretation, optimise resource extraction, and minimise environmental impact. We are seeking a dynamic and forward-thinking Geologist to join our team, bridging traditional geological expertise with the power of AI.

JOB DESCRIPTION:

As an AI-Enabled Geologist, you will be responsible for conducting geological research, analysing complex datasets, and developing AI-driven solutions for mineral exploration, environmental assessments, and land development. You will work at the intersection of geology and AI, leveraging machine learning and data analytics to extract valuable insights and drive innovation.

EDUCATION & EXPERIENCE:

  • Bachelor’s degree in Geology, Earth Sciences, Geophysics, or a related field (Master’s preferred).
  • Proven experience in geological research, fieldwork, or resource exploration.
  • Experience with geological modelling software (e.g., ArcGIS, Petrel, Surfer).
  • Familiarity with data analysis and machine learning concepts.
  • Experience with Python or R, or other programing languages for data analysis is highly valued.

TECHNICAL & SOFT SKILLS:

  • Strong analytical and problem-solving skills, with a focus on data-driven decision-making.
  • Proficiency in rock, mineral, and soil analysis techniques.
  • Ability to interpret and visualise complex geological and AI-generated data.
  • Excellent communication and report-writing skills, with the ability to convey technical information to diverse audiences.
  • Ability to work in diverse and remote field locations.
  • Knowledge of environmental regulations and safety procedures.
  • A willingness to learn and implement new AI technologies.

PREFERRED QUALIFICATIONS (OPTIONAL):

  • Professional certification (e.g., Chartered Geologist, Fellow of the Geological Society).
  • Experience in oil and gas, mining, or environmental consultancy.
  • Experience with drone mapping and remote sensing technology.
  • Experience with training and utilising machine learning models.
Responsibilities
  • Geological Fieldwork & Data Collection: Conduct geological field studies, site inspections, and collect samples (rock, soil, water) while ensuring meticulous data integrity for AI training.
  • AI-Driven Data Analysis: Utilise machine learning algorithms and AI tools to analyse geological data, including seismic data, geochemical analyses, and remote sensing imagery.
  • Geological Modelling & Prediction: Develop and refine geological models using AI to predict resource distribution, identify potential hazards, and optimise exploration strategies.
  • GIS & Spatial Analysis with AI: Integrate GIS software with AI techniques to create advanced spatial models and visualisations for enhanced decision-making.
  • Environmental Impact Assessment with AI: Apply AI to analyse environmental data, monitor changes, and predict potential impacts related to land use and resource extraction.
  • Reporting & Communication: Prepare detailed reports and presentations summarising findings, recommendations, and AI-driven insights for stakeholders.
  • Collaboration & Innovation: Work closely with AI engineers, data scientists, and other geologists to develop and implement innovative solutions.
  • Technology Integration: Stay updated with the latest advancements in AI, machine learning, and geological technologies, and identify opportunities for integration.
  • Data Management & Quality Control: Ensure the quality and consistency of geological data for AI model training and analysis.
  • Optimising resource extraction utilising AI driven analysis.
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