RFQ: Lead Data ScientistAUSTRALIAN CITIZENS ONLY!No Permanent Residents. No Work Visas. Must be able to obtain and maintain Baseline Security Clearance.RFQ Details:ID: LH-02162Closing Date: Friday, 28 March 2025 11:59pm, Canberra timeClient Information:Department: Department of Industry, Science and ResourcesContract Information:Start Date: Monday, 21 April 2025Initial Duration: 12 monthsExtension Term: 12 monthsNumber of Extensions: 2Maximum Number of Candidates per Seller: 2Role Information:Experience Level: Lead - EL1 equivalentLocation of Work: ACTWorking Arrangements: OnsiteRole is Canberra-based; flexibility options may be considered for the right candidate.Remote working arrangements may be approved on a case-by-case basis.Candidates should indicate their desired work location if outside ACT/Canberra.Maximum Hours: 40 hours per weekSecurity Clearance: Must be able to obtain and maintain Baseline clearance (sponsorship available, must be obtained within 3 months of commencement).Job Details:Position: Lead Data ScientistDepartment: Chief Information Officer Division (CIOD), Department of Industry, Science and ResourcesKey Duties and Responsibilities:Work with business areas and project teams to deliver data science solutions.Collaborate with supporting technical teams to ensure governance and security compliance.Develop and maintain project documentation, including detailed designs, technical configurations, and build guides.Our ideal candidate will:Have experience in DevOps and MLOps as applied to data science, particularly using Azure Machine Learning Studio and Cognitive Services.Be skilled in delivering data science solutions with Microsoft technologies.Apply judgment and critical thinking to solve complex problems.Have demonstrated experience working in government sector projects and small teams.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:Azure ML Expertise: Minimum 2 years of design and working knowledge in Azure Machine Learning, including ML model training, validation, and implementation.MLOps Experience: Practical experience managing ML model lifecycles, including classification, regression, clustering, computer vision, and Generative AI/NLP models.Generative AI & NLP: Hands-on experience designing Generative AI solutions using open-source LLMs, particularly with Azure or OpenAI Service.Model Deployment: Experience developing and deploying ML models as online endpoints, inferencing APIs, and integrating data assets with Azure Blob Storage and Data Lake.Prompt Engineering: Experience in LLM prompt engineering, Retrieval Augmented Generation (RAG) patterns, and factual grounding techniques.Cloud-Based Development: Proven experience developing and deploying Python web applications using Azure Cloud.Desirable Criteria:Azure Data Scientist Certification
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