LEAD DATA SCIENTIST

Balance Recruitment · Melbourne VIC 3004 · Full time
Posted 2d ago

LEAD DATA SCIENTIST

Balance Recruitment
~$120k - $150k(Estimated)
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KEY POINTS WE FOUND
  • Lead the design and implementation of AI and machine learning solutions.
  • Oversee data acquisition, integration, transformation and feature engineering across complex datasets.
  • Guide the deployment, integration, monitoring and continual improvement of analytical and AI/ML solutions.

Join our client’s team as a Lead Data Scientist and help apply advanced analytics, artificial intelligence and machine learning to complex business challenges. You’ll lead end-to-end data science delivery, turning structured and unstructured data into reliable solutions, practical insights and better-informed decisions.

This is a senior, hands-on leadership opportunity within a major government service delivery organisation supporting Australians nationwide. Work is available in the capital city of selected states: Queensland, ACT, Victoria, South Australia or Tasmania. The role is hybrid, with an initial six-week onsite period before flexible hybrid arrangements may be considered. Applicants must be able to obtain Baseline security clearance.

What you’ll do

  • Lead the design and implementation of AI and machine learning solutions, balancing grounding, latency and cost against business needs.
  • Oversee data acquisition, integration, transformation and feature engineering across complex datasets.
  • Lead experimentation, proof-of-concept work, AI training and model validation to assess feasibility, value and operational suitability.
  • Develop evaluation frameworks covering model performance, business outcomes, safety and ongoing operational monitoring.
  • Guide the deployment, integration, monitoring and continual improvement of analytical and AI/ML solutions throughout the model lifecycle.
  • Define modelling approaches and apply suitable statistical and machine learning methods to complex problems.
  • Identify trends, drivers and actionable insights, translating technical findings into clear recommendations for stakeholders.
  • Establish responsible AI controls and model assurance, including bias, fairness, explainability and safety assessments.
  • Maintain robust documentation, review and approve team deliverables, and lead team priorities and resource allocation.

What you’ll bring

  • Senior-level data science expertise, including statistical modelling, machine learning and advanced analytical methods.
  • Experience designing, evaluating and optimising generative AI solutions, including large language models, retrieval augmented generation (RAG), prompt engineering or agentic AI workflows.
  • Strong capability in exploratory analysis of large or complex datasets, data modelling, data preparation and analytical platforms.
  • Confidence shaping research questions and analytical solutions in partnership with stakeholders.
  • Sound judgement in applying statistical methods and specialist analysis, and the ability to improve processes, tools and products.
  • Leadership skills to guide a team, build capability, ensure quality and deliver outcomes in a complex environment.
  • Awareness of data use and reuse, relevant legislative requirements, and organisational guidance.

Why consider this opportunity?

Bring your technical leadership to work with meaningful public impact. You’ll help advance responsible, effective data science and AI capabilities, influence solutions from experimentation through to operation, and collaborate with stakeholders on work that supports essential services for Australians.

Initial contract of up to 12 months. Hybrid arrangements may be considered after the first six weeks onsite, subject to business requirements and reasonable travel distance to the relevant capital city.

Please apply now to Jin quoting JL680439.

Balance Consulting Services is committed to equal opportunity employment. We celebrate diversity and encourage people from all sections of our community to apply for.

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Skills

0 of 25 matched
Advanced analyticsAgentic ai workflowsAnalytical thinkingArtificial intelligenceBias and fairness assessmentCollaborativeData acquisitionData integrationData transformationEvaluation frameworksExperience with large language modelsExplainability and safety in aiFeature engineeringLeadershipMachine learningModel deploymentModel validationProblem solvingPrompt engineeringResponsible aiRetrieval augmented generation (rag)Security clearance (baseline)Stakeholder engagementStatistical modellingTeam management

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