Data Scientist: ML & Predictive Analytics (6-Month)

Sharp and Carter; Brisbane; On-site; AUD 90,000 - 130,000; Full time

Job summary

Sharp and Carter is seeking an experienced Data Scientist to contribute to a major Data & Analytics uplift program in Queensland. You will work across complex datasets to develop ML models, predictive analytics and decision-support capabilities aimed at measurable business outcomes.

The role focuses on predictive modelling, risk identification, and building reliable AI/ML solutions, with collaboration across data professionals, SMEs and stakeholders to improve decision making and operational

Qualifications

• Demonstrated experience applying ML to complex datasets and delivering measurable outcomes.
• Strong statistical and predictive modelling capabilities.
• Hands-on experience with Python, SQL and Azure Databricks; deployment of AI/ML solutions via CI/CD.

Responsibilities

• Develop predictive models using classification and regression techniques.
• Apply ML to large, complex datasets to identify trends and opportunities.
• Develop analytical solutions using Python/SQL on Azure Databricks.
• Contribute to deployment of AI/ML solutions through CI/CD processes.
• Monitor and optimize production ML solutions for performance and cost.
• Explore NLP and text analytics to extract insights from unstructured data.
• Translate findings into actionable insights for business stakeholders.

Skills

Python

SQL

Machine learning

NLP

Education

Data Science/Statistics/Mathematics related degree

Tools

Azure Databricks

CI/CD pipelines

Job description

Sharp and Carter is seeking an experienced Data Scientist to contribute to a major Data & Analytics uplift program in Queensland. You will work across complex datasets to develop ML models, predictive analytics and decision-support capabilities aimed at measurable business outcomes.

The role focuses on predictive modelling, risk identification, and building reliable AI/ML solutions, with collaboration across data professionals, SMEs and stakeholders to improve decision making and operational