Principal Data Scientist

Candidate Description

Driving enterprise AI transformation through practical, measurable delivery, the candidate is a Principal Data Scientist and AI/ML engineering leader with 11+ years of experience. They hold a PhD in Data Science focused on NLP, deep learning and traffic forecasting, supported by an electrical and electronic engineering foundation. Their experience spans a facilities management company, a mental wellness technology company, a financial AI company, a technology company, a university, a telecommunications and IT solutions company, and another university. Core expertise includes generative AI, large language models, agentic architectures, multi-agent RAG, predictive analytics and intelligent document processing. They combine advanced analytical capability with technical consulting and executive stakeholder engagement across facilities management, financial automation, fintech, transportation research and embedded engineering.

Azure-powered document intelligence and production-oriented AI delivery underpin their strongest commercial achievements. At a facilities management company, they led nine data scientists and ML engineers in delivering invoice automation that reduced accounts payable processing time by 43% and identified approximately $17.58 million in annual discrepancies. They also architected a multi-agent RAG chatbot that reduced job creation time by 63% and deployed LLM-based defect detection that reduced reactive maintenance jobs by 23%. Their technical toolkit spans Azure Machine Learning, Azure Cognitive Services, Azure OpenAI, AWS SageMaker, GCP, Databricks, Python, SQL, R, PyTorch, TensorFlow, Keras and Scikit-learn. Generative AI capabilities include LangChain, LangGraph, CrewAI, Hugging Face, OpenAI, Gemini and DeepSeek, complemented by semantic search, vector indexing, prompt engineering and hallucination mitigation. They support delivery through Docker, Kubernetes, REST APIs, FastAPI, Git, CI/CD, MLflow, Weights & Biases and model lifecycle governance, with Agile/Scrum practices and analytics visualization using Power BI, Tableau, Plotly, Seaborn and D3.js.

Their leadership approach connects technical rigor with stakeholder confidence and business priorities. They have mentored cross-functional teams, delivered AI upskilling programs and defined product roadmaps aligned with business KPIs. Experience addressing class imbalance, overfitting, model validation, technology risk and responsible AI supports dependable enterprise implementation. Research achievements include traffic prediction accuracy of up to 87%, an 18% improvement in congestion forecasting and recognition through a research and innovation award in 2019. Their progression from embedded systems and hardware research to enterprise AI leadership demonstrates adaptability across software, data, infrastructure and operational environments. As an Australian citizen based in Melbourne and open to relocation, they offer employers a combination of scientific depth, hands-on engineering and commercially focused AI leadership.