This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Science Lead based in Australia.

This is a strategic technical leadership opportunity within a high-impact AI program operating in a highly regulated environment.
You will lead the architecture and evolution of machine learning and GenAI capabilities supporting automated regulatory validation workflows.
The role combines hands-on expertise in LLM systems with architectural ownership, evaluation, and productionisation.
You will design sophisticated RAG pipelines and retrieval strategies while ensuring AI workflows are reliable, reproducible, explainable, and traceable.
Working across data science, engineering, product, and compliance teams, you will influence key technical decisions and drive the platform toward enterprise scale.
The position offers the opportunity to shape production-grade AI systems where quality, governance, and measurable performance are critical.
It is well suited to an experienced data science leader who enjoys solving complex problems and turning advanced AI capabilities into robust business solutions.

Accountabilities

• Design, evolve, and oversee AI and machine learning architecture supporting automated compliance validation workflows.
• Design and optimize LLM-powered retrieval and validation pipelines, including Retrieval Augmented Generation (RAG) architectures.
• Establish evaluation frameworks, benchmarking approaches, and continuous improvement processes to measure and enhance AI system performance.
• Develop explainability, traceability, and validation mechanisms that support regulatory and compliance requirements.
• Collaborate closely with ML Engineers and Backend Engineers to productionize AI components and integrate them into reliable enterprise systems.
• Drive technical decisions around embeddings, vector databases, retrieval strategies, and related AI infrastructure.
• Ensure AI workflows are reproducible, testable, maintainable, and aligned with high-quality engineering standards.
• Support the scaling of AI capabilities from individual workflows into a robust, enterprise-grade platform.
• Lead technical discussions and align architectural decisions across product, engineering, data science, and compliance stakeholders.
• Identify opportunities to improve system reliability, model performance, scalability, and operational effectiveness.

Requirements

• Strong hands-on experience in Data Science, applied Machine Learning, and production AI systems.
• Proven experience designing and deploying LLM-based solutions in production environments.
• Practical experience with Retrieval Augmented Generation architectures and LLM-driven retrieval workflows.
• Experience working with vector databases, embeddings, and embedding-generation pipelines.
• Strong Python programming skills and familiarity with modern machine learning and AI frameworks.
• Demonstrated experience designing model evaluation, benchmarking, and validation frameworks.
• Understanding of production-grade AI architecture, with an ability to move beyond proof-of-concept implementations.
• Experience operating in regulated, compliance-heavy, or otherwise highly governed environments.
• Strong ownership mindset, architectural judgment, and confidence making and influencing technical decisions.
• Excellent communication and collaboration skills, with the ability to work effectively across technical and non-technical teams.
• Experience with pharmaceutical, healthcare, or other regulated industries is a strong advantage.
• Familiarity with explainable AI methodologies, document intelligence, or NLP-heavy pipelines is beneficial.
• Experience building enterprise-scale AI platforms is highly desirable.

Benefits

• Competitive salary package.
• Opportunity to work on international, high-impact AI initiatives within a regulated enterprise environment.
• Comprehensive healthcare coverage.
• Long-term B2B contract with a stable project pipeline.
• Fully remote working model.
• Opportunity to work with modern LLM, RAG, machine learning, and AI platform technologies.
• Significant technical ownership and influence over enterprise AI architecture and delivery.