Lead Data Scientist – AI, Machine Learning

Lead Data Scientist – AI, Machine Learning & Advanced Analytics

Description of the Role

- Design, develop, and implement advanced data science, artificial intelligence, and machine learning solutions to address complex business challenges and improve organizational performance.
• Apply statistical, mathematical, analytical, and machine learning techniques to extract insights from structured and unstructured data and support data-driven decision-making.
• Collaborate with business stakeholders, data engineers, and technology teams to translate business requirements into scalable AI, machine learning, and analytics solutions across cloud and on-premises environments.

Core Responsibilities

- Collect, prepare, cleanse, validate, and analyze large and complex datasets to identify trends, patterns, anomalies, and opportunities for business improvement.
• Design, develop, test, evaluate, and optimize machine learning, predictive analytics, and artificial intelligence models using statistical and data science methodologies.
• Build and implement Retrieval-Augmented Generation (RAG) solutions and generative AI applications leveraging Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related AI platforms.
• Develop, train, evaluate, and maintain custom machine learning and computer vision models using Azure-native AI services, YOLO frameworks, and on-premises machine learning infrastructure.
• Establish and govern MLOps practices, including model lifecycle management, automated training and deployment pipelines, model monitoring, performance evaluation, and continuous improvement.
• Analyze business problems and translate them into data science, analytics, and AI opportunities that deliver measurable business outcomes and operational efficiencies.
• Collaborate with data engineering, infrastructure,



and architecture teams to define data requirements, solution architecture, and deployment strategies for enterprise AI and analytics solutions.
• Interpret analytical findings and model outputs, communicate insights to stakeholders, and provide evidence-based recommendations to support strategic and operational decision-making.

Key Responsibilities

Core Responsibilities

- Collect, prepare, cleanse, validate, and analyze large and complex datasets to identify trends, patterns, anomalies, and opportunities for business improvement.
• Design, develop, test, evaluate, and optimize machine learning, predictive analytics, and artificial intelligence models using statistical and data science methodologies.
• Build and implement Retrieval-Augmented Generation (RAG) solutions and generative AI applications leveraging Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related AI platforms.
• Develop, train, evaluate, and maintain custom machine learning and computer vision models using Azure-native AI services, YOLO frameworks, and on-premises machine learning infrastructure.
• Establish and govern MLOps practices, including model lifecycle management, automated training and deployment pipelines, model monitoring, performance evaluation, and continuous improvement.
• Analyze business problems and translate them into data science, analytics, and AI opportunities that deliver measurable business outcomes and operational efficiencies.




• Collaborate with data engineering, infrastructure, and architecture teams to define data requirements, solution architecture, and deployment strategies for enterprise AI and analytics solutions.
• Interpret analytical findings and model outputs, communicate insights to stakeholders, and provide evidence-based recommendations to support strategic and operational decision-making.

Skill & Experience

Specific Skills, Qualifications, and Experience required for the Position

- 8–12 years of experience in Data Science, Machine Learning, Artificial Intelligence, Advanced Analytics, or related disciplines, including delivery of enterprise-scale AI and analytics solutions.
• Solid expertise in statistical analysis, predictive modelling, machine learning algorithms, data preparation, model evaluation, and analytical problem-solving techniques.
• Advanced programming experience in Python and PySpark, with proven capability in developing, training, testing, and deploying machine learning models in production environments.
• Hands-on experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, Microsoft 365 Copilot, RAG architectures, and computer vision solutions using Azure AI services or YOLO frameworks.
• Demonstrated ability to translate business challenges into analytical solutions, facilitate stakeholder workshops, and communicate complex technical concepts to both technical and non-technical audiences

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📌 Lead Data Scientist – AI, Machine Learning (New South Wales)
🏢 DoublU
📍 New South Wales