Lead Data Scientist

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 Lead 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 Lead Data Scientist will lead the design and delivery of data science activities and solutions across the end-to-end model lifecycle. Desirable knowledge/Qualifications: •  Advanced degree in computer science, statistics, mathematics, or a related field, with a focus on data science, machine learning, or artificial intelligence. • Experience (typically 3+ years) in data science with a proven track record. • Data science, analytical and modelling techniques including artificial intelligence and machine learning and their application 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. Key duties and responsibilities Duties may include all or some of the following: • 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, 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. • 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. • Artificial intelligence, machine learning, statistical modelling and analytical techniques, and their application to complex business problems and datasets. • 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. • 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. Criteria The buyer has specified that each candidate must provide a one page pitch to address all criteria specified. This is equal to 5000 characters. Essential criteria 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) Desirable criteria 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) Opportunity summary Sellers can submit Up to 3 candidates Number of sellers invited More than 10 Number of candidates submitted Fewer than 10 RFQ closing time 02 Weeks 00 Days 12 Hours Send us feedback About Accessibility Privacy Terms of use Disclaimer and copyright An initiative of the Digital Transformation Agency The Australian Government acknowledges the Traditional Owners of Country throughout Australia and acknowledges their continuing connection to land, waters and community. We pay our respects to the people, the cultures and the Elders past and present. © Commonwealth of Australia