Geotechnical Data Science Intern — Ground Risk Analytics

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 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.