Data Scientist

One of our federal clients is seeking an experienced Data Scientist to join the team.

About the role

Data scientists apply advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning and other uses. The Data Scientist (EL1) will lead the application of analytical, statistical and computational methods, including artificial intelligence, leveraging structured and unstructured data to supplement operational workflows and inform strategic decision-making. Working closely with stakeholders to understand their needs, the Data Scientist will lead the design and delivery of data science activities and solutions across the end-to-end model lifecycle.

Key duties and responsibilities

• Lead and design the implementation of artificial intelligence and machine learning solutions that optimise for grounding, latency, and cost, supporting branch, division and agency objectives.
• Lead data acquisition, integration, transformation and feature engineering for complex datasets.
• Lead experimentation, proof-of-concept activities and model validation to assess technical feasibility, business value and operational suitability.
• Lead AI training activities using appropriate and representative datasets to deliver reliable, accurate and operationally effective outcomes.
• Design and implement robust qualitative and quantitative evaluation frameworks, including model performance, business outcome measurement, safety assessment and operational monitoring.
• Lead the deployment, integration, monitoring and continual improvement of analytical, artificial intelligence and machine learning solutions across the model lifecycle.
• Lead advanced analysis to identify trends, drivers and actionable insights that support data-driven decision-making.
• Define modelling approaches and select appropriate statistical and machine learning techniques for complex problems.
• Define and apply analytical and modelling approaches for complex business problems and drive model performance improvement.
• Engage stakeholders to understand business needs, shape analytical solutions and translate complex technical concepts into clear insights and recommendations.
• Lead and maintain documentation to support reproducibility and knowledge sharing.
• Lead the development and implementation of Responsible AI controls, model assurance activities and governance processes, including bias, fairness, explainability and safety assessments.
• Provide quality assurance reviews and approve the deliverables of the team.
• Lead and manage team operations to deliver business and strategic outcomes, setting priorities and allocating resources effectively.

Essential criteria

• Data use and re-use: Level 2 (DCF) — Understands and can articulate the value of data in terms of use and re-use Implements a variety of techniques to ensure data is open and can be used beyond the specific purpose for which it was collected, in accordance with legislative requirements and organisational guidelines (includes data sharing legislation)
• Exploratory data analysis: Level 3 (DCF) — Is highly competent at performing exploratory analysis on large/complex datasets Can build capability of others in exploratory data analysis techniques Can write custom scripts and code in a programming language to conduct complex analytical tasks Maintains understanding of new trends and innovations relating to exploratory data analysis (including Artificial Intelligence-based technologies) and develops skills in these where relevant
• Identify research questions: Level 3 (DCF) — Uses a range of techniques to assess data needs and identify gaps, towards the formulation of appropriate research questions
• Improvement and Innovation - Data processes/systems and tools/products: Level 2 (DCF) — Can identify deficiencies in current processes/systems and tools/products, gain the required approval to make changes, and lead the implementation of those changes Can identify and harness opportunities to create efficiencies and effective new processes/systems and tools/products Can develop a test scenario and lead systems testing, following organisational procedures and protocols and analyse the results
• Specialist data analysis: Level 2 (DCF) — Can develop, fit, diagnose, and troubleshoot a model in a new data analysis scenario that requires a specialist method (e.g., time series forecasting or geospatial analysis) Is able to understand and amend existing code (programming language) to conduct specialist data analysis
• Statistical concepts and methodologies: Level 3 (DCF) — Is an expert resource on statistical concepts and provides advice on the proper use of statistical methods Leads efforts to apply good statistical practice Can build capability of others in understanding and applying statistical concepts and methods
• Statistical data analysis: Level 3 (DCF) — Contributes to the development of new functionality for statistical analysis applications, which enables new ways of doing things Can write custom scripts and code in a statistical computing language to conduct complex analytical tasks Maintains understanding of new trends and innovations relating to statistical data analysis (including Artificial Intelligence-based technologies) and develops skills in these where relevant
• Subject matter expertise: Level 2 (DCF) — Has a comprehensive knowledge of the subject matter area associated with the data use and can readily identify the parameters of the subject matter that influence the use of the data
• Value organisational data as assets: Level 2 (DCF) — Has extensive knowledge of the organisation's data assets, including a comprehensive understanding of how their fitness for purpose translates to value for the organisation Promotes opportunities for using data to support decision making, advice and research Considers the broader environment and context when drawing or interpreting insights from data. Uses these insights to make informed, evidence-based decisions and recommendations, and act accordingly

Technical skills, knowledge and qualifications

• Artificial intelligence, machine learning, statistical modelling and analytical techniques, and their application to complex business problems and datasets
• AI training
• Designing, evaluating and optimising generative AI solutions including large language models
• retrieval augmented generation (RAG)
• prompt engineering and agentic AI workflows
• data modelling techniques, data science platforms, analytical environments, best practice data cleaning.

Working arrangements

Hybrid

The Labour Hire Worker is expected to work onsite for the first 6 weeks before a hybrid flexible working arrangement can be considered, unless otherwise negotiated.

A hybrid flexible working arrangement may then be considered

Estimated start date: 1 December 2026

Initial contract duration: Up to 12 months + extensions 

Application requirements

• Latest copy of your CV
• A suitability statement addressing the skills/experiences.

If you believe you are the right fit, apply today.

If you know someone who can be a great fit, refer them via our referral page on the website.

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