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Senior Data Scientist @ Microsoft | Production GenAI | Technical Lead | MLOPs | Evaluation | Enterprise AI
With 10+ years in software and AI, I am a Technical Lead and Senior Data Scientist at Microsoft focused on building production systems that deliver tangible business value. I combine engineering rigor with data science fundamentals to ensure AI solutions hold up in the real world. Delivering Outcomes - I lead end-to-end engagements for major enterprise customers, architecting solutions that scale from PoC to thousands of users. My focus is always on ensuring technical deliverables align tightly with measurable business goals. Architecture & MLOps - I bring a software engineering mindset to AI, specializing in "DS inner-loops" and MLOps foundations. I replace ad-hoc experimentation with CI/CD, automated tracking, and rigorous testing strategies. Whether for edge-based computer vision or scalable RAG architectures, I prioritize observability, reliability, and security. Advisory & Strategy - I act as a lead technical advisor to engineering crews and leadership, helping navigate AI ambiguity. I am passionate about upskilling teams, defining Responsible AI guardrails, creating accelerators adopted across Microsoft, and contributing features back to Azure products based on real-world needs.
Monash University
Master's degree, Information Technology
January 1, 2013 – January 1, 2014
Monash University
Bachelor of Science (B.Sc.), Mathematics
January 1, 2005 – January 1, 2010
Microsoft
Senior Data Scientist
January 1, 2022 – Present
Remote
ME Bank
Machine Learning Engineer (Project Lead)
August 1, 2021 – December 1, 2021
AGL Australia
Machine Learning Engineer
April 1, 2021 – July 1, 2021
Mutual Information Technologies
Data Scientist
February 1, 2020 – April 1, 2021
Melbourne, Victoria, Australia
Monash University
Data Scientist
February 1, 2017 – November 1, 2019
Melbourne, Australia
Thales
Software Engineer (Air Traffic Control Systems)
January 1, 2015 – February 1, 2017
Thales
Intern Software Engineer (Air Traffic Control Systems)
September 1, 2014 – December 1, 2014
Monash University
Research Machine Learning Engineer
January 1, 2014 – July 1, 2014
Faculty of IT, Monash University, Melbourne
PhotoStream.Live (personal side project)
December 1, 2019 – Present
We ran out of money for a photographer at our wedding reception, so I created a web app to crowd-source photos from our guests. Afterwards, I ran this for several friends' weddings and events. Recently I took the time to build out the idea as a scalable web application that can accept customers internationally. The app allows users to host a live photo stream for their event where guests can send in live photos and videos. The website is built with Django (Python), Javascript and CSS. The app component is primarily built on AWS Lambda and also utilises MySQL, Redis, S3, Rekognition on AWS and Firestore, Vision APIs on Google Cloud. In creating this app, not only did I upskill in web development and several cloud technologies, I also learned a lot about design, UX, feature prioritisation, marketing, sales and business strategy.
Research and development of NLP machine learning algorithms resulting in an industry first analytics capability
March 1, 2017 – November 1, 2019
Delivered in my role as a Data Scientist at Monash University Ground up research, development, training, testing and production deployment of NLP machine learning algorithms to automate information extraction, categorical classification and sentiment analysis on a large (>500k samples) customer dataset related to infrastructure maintenance. I also built out automated reporting and alerting for staff and management based on the outputs of the productionised models. This work was used internally by the university to power analytics for various infrastructure and property management teams. As an industry-first, this work was also presented in conferences and to external organisations which resulted in trial applications with multiple commercial organisations in the facilities management industry.
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Algorithms: Design and Analysis, Part 1
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Learning Data Science: Tell Stories With Data
June 24, 2026 – Present
Natural Language Processing
Coursera
June 24, 2026 – Present
Human-Computer Interaction
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Bayesian Statistics: Techniques and Models (with Honors)
Coursera
June 24, 2026 – Present
Computational Investing, Part I
Coursera
June 24, 2026 – Present
Computing for Data Analysis
Coursera
June 24, 2026 – Present
Web Intelligence and Big Data
Coursera
June 24, 2026 – Present
Bayesian Statistics: From Concept to Data Analysis
Coursera
June 24, 2026 – Present
Startup Engineering
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse experience across large enterprises (Microsoft, Thales), financial institutions (ME Bank), energy companies (AGL), and academic research (Monash University) demonstrates adaptability to various organizational cultures. Their involvement in personal projects and hackathons indicates a proactive, innovative, and continuous learning mindset. The focus on Responsible AI and mentoring aligns with a culture that values ethical practices and knowledge sharing. While the target role is 'Data Analyst', the candidate's background is heavily skewed towards Data Scientist/ML Engineer, which might indicate a potential mismatch in day-to-day responsibilities if the analyst role is purely descriptive analytics.
Soft Skills & Operational Fit
The candidate's experience at Microsoft and ME Bank highlights leadership, project management, and technical advisory skills. Their personal project demonstrates initiative, business acumen, and a holistic approach to product development. The ability to upskill team members and champion Responsible AI indicates a collaborative and ethical approach to work. The diverse project experience suggests adaptability and a strong problem-solving mindset.