Bio

Data Scientist & AI Engineer

I am a Data Scientist and AI Engineer with 10 years’ experience (7 in Australia), specialising in agentic applications, machine learning, language models, data-driven decision making, and AI governance. I hold a PhD in IT and Analytics and bring a combination of technical expertise and strategic vision across domains such as finance, IoT supply chain, digital health and cloud data platforms. I have led the development of privacy-preserving GenAI and ML systems, including model deployment and monitoring, applied to demand forecasting, customer segmentation, and anomaly detection, adhering to regulations and standards in data privacy (GDPR, HIPAA, APP) and responsible AI frameworks. In addition to my technical expertise, I have led agile data science teams, mentored junior engineers, and driven data solutions that deliver measurable business impact and support organisational objectives.

01

Selected work

Three portfolio studies spanning predictive ML, retrieval, and agent workflows.

2026

Customer-interaction RAG agent

A retrieval and tool-use workflow for policy and account questions, with citations, scoped lookups, and human escalation.

  • RAG
  • Tool use
  • Guardrails
  • Evaluation
Read case study
Applied AI · GenAISynthetic / simulated evidence
Fictional aged-care review queue showing HIGH, MODERATE, and LOW risk bands, factor explanations, and safety warnings

2026

Seven-day fall-risk prediction

A risk-ranking workflow for deciding which aged-care residents should receive review first.

  • Tabular ML
  • Calibration
  • Capacity analysis
  • Monitoring
Read case study
Predictive ML · safetySynthetic / simulated evidence

Synthetic / simulated evidence All outcomes are synthetic or simulated portfolio evidence.

02

Focus areas

The methods and checks I use around a model.

01

Predictive ML

Baselines, calibration, slices, and review capacity.

02

Retrieval and agents

Retrieval, tools, citations, and escalation.

03

Evaluation

Metrics tied to the decision being supported.

04

Monitoring

Drift, missingness, failures, and response.

05

Human review

Clear boundaries, synthetic data, and limits.

03

Approach

Building useful systems with clear boundaries.

My work combines technical delivery with governance, team leadership, and clear communication of model limits.

Each case study shows the decision, method, evaluation, failure cases, and limitations.