Intern - Geotechnical/ Geophysics engineering

EQUANS; Sydney; On-site; AUD 90,000 - 130,000; Full time

Job summary

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.

Qualifications

• Undergraduate degree in a relevant data/AI field.
• Experience or modules in geotechnical engineering, geophysics or civil engineering preferred.
• Strong Python skills and knowledge of data science libraries (NumPy, pandas, scikit-learn).
• Proactive, innovative, independent and curious.

Responsibilities

• Develop, refine and validate data analytics and ML models for ground conditions related to piling refusal.
• Consolidate and manage large datasets from boreholes, test pits, geophysical surveys and piling records.
• Build predictive models and visualization tools to improve ground risk, budgeting and planning.
• Adapt existing model from France to Australian conditions and requirements.
• Collaborate with engineering, construction and innovation teams to translate findings into practical recommendations.
• Prepare technical reports, presentations and documentation for internal stakeholders.
• Liaise with consultants and attend site visits.

Skills

Python

Data science libraries

Geotechnical exposure

Analytical reasoning

Education

Undergraduate in Data Science/AI/CS

Job description

Equans Solar & Storage will imminently launch its Lightspeed Solar Portfolio (LSP) program, a utility-scale testbed environment to trial and deploy innovation projects that help to lower the cost of constructing and operating large-scale solar and storage projects. LSP will be part of ARENA’s Advancing Renewables Program, contributing to ARENA’s goals to reduce LCOE of renewables technologies in Australia.

The team you’ll be joining

Our Australian team consists of approximately 50 highly skilled engineers, and it is expanding rapidly due to the growth of large-scale hybrid projects.

You will be a part of our growing Innovations team tasked with the implementation of our LSP program, specifically in the Digital and Construction workstreams.

Want to learn more about our projects? :

Solar & Storage Solutions for Energy Efficiency | Equans

What you’ll be leading

You’ll report to the Innovation leader, our key person in Australia managing the LSP program, and will be working closely with our Civil team based in Sydney.

Your rôle and responsibilities:

• Primary role: Develop, refine and validate data analytics and machine learning models to identify ground conditions associated with piling refusal on Equans current and future solar and storage projects.
• Consolidate and manage large datasets from intrusive investigations (e.g. boreholes, test pits), non-intrusive investigations (e.g. geophysical surveys), and piling installation records.
• Build predictive models and data visualization tools to improve ground risk assessment, project budgeting and construction planning.
• Continue and enhance an existing preliminary model previously developed by Equans in France, adapting it to Australian ground conditions and project requirements.
• Collaborate with engineering, construction and innovation teams to translate technical findings into practical recommendations for project development and delivery.
• Prepare technical reports, presentations and documentation to communicate methodologies, assumptions and key findings to internal stakeholders.
• Liaise with consultants, performing technical analysis of consultants proposals, attend site visits/occasional travels to site

The profile we’re looking for

• Undergraduate in Data Science/AI/Computer Science or similar;
• Has preferably undertaken modules or has experience with geotechnical engineering, geophysics, and/or civil engineering;
• Proficient in Python and common data science libraries
• Proactive, innovative, independent and curious