Geotechnical Data Science Intern in Sydney

Geotechnical Data Science Intern in Sydney

Sydney Internship 28.8 - 35.2 $ / hour (est.) On-site
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At a Glance

  • Tasks: Develop analytics and ML models to assess ground conditions for solar projects.
  • Company: Join EQUANS, a leader in innovative data solutions.
  • Benefits: Gain hands-on experience, mentorship, and networking opportunities.
  • Other info: Dynamic team environment with opportunities for growth in the geotechnical field.
  • Why this job: Make an impact on sustainable energy projects while honing your data science skills.
  • Qualifications: Undergraduate degree in Data Science/AI/CS and strong Python skills required.

The predicted salary is between 28.8 - 35.2 $ per hour.

EQUANS invites applications for a data science focused role within its Australian Innovations team in Sydney.

You will develop analytics and ML models to assess ground conditions for piling on solar and storage projects, leveraging large datasets from boreholes, geophysics and piling records.

Ideal candidates will have an undergraduate degree in Data Science/AI/CS, experience with geotechnical contexts, and strong Python skills plus knowledge of common data science libraries.

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Geotechnical Data Science Intern in Sydney employer: EQUANS

Equans is an exceptional employer for Senior Electricians, offering a dynamic work environment where you can engage with critical infrastructure and advanced electrical systems across Sydney and the ACT. With a strong focus on employee growth, competitive remuneration, and structured technical training, you'll have the opportunity to develop your skills while enjoying significant autonomy in your role. Join a supportive team that values your expertise and provides pathways for career advancement in a global organisation committed to safety and excellence.

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Contact Details:

EQUANS Recruitment Team

We think you need these skills to ace Geotechnical Data Science Intern in Sydney

Data Science
Machine Learning (ML)
Python
Geotechnical Knowledge
Data Analysis
Experience with Large Datasets
Familiarity with Data Science Libraries