Lead Data Scientist

LH-08184

12 months

Lead - EL1 equivalent

You'll lead data science at Services Australia, applying analytical, statistical and computational methods, including artificial intelligence, to structured and unstructured data so that operational workflows are supported and strategic decisions are better informed. Working closely with stakeholders to understand what they need, you'll lead the design and delivery of data science activities and solutions across the full model lifecycle, from problem definition and data acquisition through to production deployment, monitoring and continual improvement. You'll set modelling approaches, choose the right statistical and machine learning techniques for complex problems, and shape AI and machine learning solutions that balance grounding, latency and cost. You'll also lead a team: setting priorities, allocating work, reviewing and approving deliverables, and keeping governance, ethical and organisational standards front and centre while turning complex technical concepts into clear insights and recommendations.

• 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 • Design and implement robust qualitative and quantitative evaluation frameworks, testing approaches and assurance processes that support reliable model performance • Lead the deployment, integration, monitoring and continual improvement of analytical, AI 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 • Ensure compliance with governance, ethical and organisational standards and manage analytical risks • 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

• Advanced degree in computer science, statistics, mathematics or a related field, with a focus on data science, machine learning or artificial intelligence • Typically 3+ years of data science experience with a proven track record • Data science, analytical and modelling techniques, including AI and machine learning, applied to complex business problems • Data modelling techniques (relational, dimensional, messaging and big data), data analysis and profiling using modern analytical tools and programming languages, and visualisation methods and tools • Industry best practice and trends to achieve business outcomes • Statistical concepts, machine learning algorithms and modelling approaches applied to complex datasets • Data science platforms, analytical environments and integration approaches that support scalable, reproducible and reliable delivery • Best-practice data cleansing, validation, quality assurance and feature preparation techniques for complex datasets

Hybrid

1. Demonstrated experience leading the development and deployment of data science solutions in complex environments, including problem definition, data acquisition, selection of analytical techniques, establishment of standards and governance, and delivery of measurable business outcomes. (SFIA: Data Science L5) 2. Demonstrated EL1-level leadership in translating complex and ambiguous business problems into machine learning and AI-enabled solutions, including stakeholder engagement, management of risks and dependencies, model or agent development, production deployment, data pipeline development, CI/CD integration, and operationalisation of analytical products, coaching and mentoring of technical staff, and influencing organisational decision-making. (SFIA: Machine Learning L4, Business Situation Analysis L5) 3. Experience designing, developing and deploying generative AI, Large Language Model (LLM) or agentic AI solutions, including evaluation, deployment, monitoring and integration with enterprise systems. (SFIA: Machine Learning L4, Artificial Intelligence and Data Ethics L4) 4. Experience designing and implementing machine learning and data engineering solutions, including model development, production deployment, data pipeline development, CI/CD integration, and operationalisation of analytical products. (SFIA: Machine Learning L4, Data Engineering L4) 5. Experience developing and maintaining analytical, machine learning or data processing code using recognised software engineering practices, including testing, version control and secure development standards. (SFIA: Programming/Software Development L3) 6. Experience establishing measures and reporting for data science and machine learning solutions, including model performance, data quality, operational risk, business value, adoption and continuous improvement outcomes. (SFIA: Data Science L5, Machine Learning L4)

1. Experience applying data governance, data management and data quality practices to ensure the availability, integrity, security and usability of data assets. (SFIA: Data Management L4) 2. Relevant qualifications or certifications in data science, machine learning, data engineering, artificial intelligence, software engineering or related disciplines. 3. Experience applying ethical, privacy and responsible AI principles to the design, development, deployment and monitoring of data science and machine learning solutions, including assessment and mitigation of risks. (SFIA: Artificial Intelligence and Data Ethics L4) 4. Experience planning and delivering analytical, data science or machine learning projects, including scope, schedule, risk, stakeholder engagement and governance management. (SFIA: Project Management L4)

Each candidate must upload a one page pitch addressing all criteria specified. This includes responding to the Job Details, Key Responsibilities, and Essential Criteria. The pitch cannot be more than 5000 characters. How to write it • Use the STAR format (Situation, Task, Action, Result) for each example so Services Australia can see exactly what you did and what it achieved • Sell your capability rather than listing duties: lead with the outcome you delivered, then show how you got there • Address each Essential Criterion by number and name the SFIA capabilities it cites (Data Science L5, Machine Learning L4, Business Situation Analysis L5, Artificial Intelligence and Data Ethics L4, Data Engineering L4, Programming/Software Development L3) • Show EL1-level leadership: leading a team, influencing decisions and translating ambiguous business problems into AI and machine learning solutions • Include a generative AI, LLM or agentic AI example covering evaluation, deployment, monitoring and enterprise integration If you have worked at Services Australia before, include with your application the branch and division you worked in, your role and your dates.