Chamil Jay (CJ)

Ph.D, B.Sc.Eng (Hons)

Data Scientist, ML and AI Engineer

Career Highlights

  • Engineering leader with deep experience delivering scalable AI/ML and data science solutions across retail, healthcare, research, and gaming.
  • Proven record of leading cross-functional teams of ML engineers, data engineers, and software engineers to design, build, and operate AI systems that deliver measurable business outcomes.
  • Strong product mindset with experience partnering with Product Managers, Architects, and Security teams to translate business goals into prioritized, high-impact initiatives.
  • Hands-on expertise in machine learning, deep learning, LLMs, RAG, and optimization, combined with practical experience in MLOps, CI/CD, DevOps, model evaluation pipelines, and production integration.
  • Experienced in improving engineering practices through automation, modern software engineering, and continuous improvement.
  • Known for coaching and mentoring high-performing teams while fostering a culture of experimentation, learning, and delivery excellence.

Relevant Work Experience

Coles

Lead Data Scientist (Dec 2021-Present) Data Scientist (Mar 2019-Nov 2021)

Led end-to-end design and delivery of scalable machine learning solutions across forecasting, pricing optimization, and real-time inference.

  • Guided Counts Model: Led development of a model to correct store inventory positions through targeted recounting, reducing counting labor by 75% compared with the previous approach.
  • Fill List Model: Developed a model to identify items requiring shelf restocking, reducing restocking labor and improving on-shelf availability.
  • Total Inventory Model: Led development of an item-store inventory estimation model that provides accurate stock positions for guided tasking and automated inventory corrections.
  • Auto Adjustment Model: Led a cross-functional team to develop a model that automatically corrects inventory records for more than 2,000 items daily without manual effort.
  • Lost Sales Model: Contributed to a machine learning model that retrospectively estimates lost sales resulting from product unavailability; insights are used to improve availability.
  • Dynamic Markdown Model: Designed and delivered a suite of models to optimize markdown pricing for short-coded products, with an estimated annual benefit exceeding $5 million.
  • Production Planning Model: Developed an optimization algorithm to schedule in-store deli production efficiently, improving availability while reducing labor requirements.

Crown Resorts Melbourne

Data Scientist (May 2018-Mar 2019)

Led development and integration of AI solutions for marketing and customer engagement, building pipelines from the ground up and integrating them into business processes.

  • Customer lifetime value model: Created ML models to predict visitation, spend, and campaign responsiveness, improving campaign effectiveness by more than 30%.
  • Gambling risk detection: Developed a deep learning model to flag patrons exhibiting behavior indicative of potential gambling issues, enabling more targeted and efficient support interventions.
  • Patron journey models: Built a suite of models to predict customer journeys and identify patrons likely to become high-value customers.
  • ML pipeline scheduling platform: Built an automated platform from the ground up to run models end to end on schedule, including reporting, dependency handling, and logging.

Melbourne Networked Society Institute (MNSI), The University of Melbourne

Data Scientist/Researcher (Jan 2014-May 2018)

Served as Tech Lead at MNSI, providing technical expertise across interdisciplinary projects.

  • Anomaly Detection in IP Networks: Developed an RNN-based model to detect anomalies in software-defined networks at the edge.
  • Built Environments and Emotional Well-Being: Implemented facial recognition and Twitter data mining to analyze how built environments influence emotions.
  • Automated Opinion Mining of Political Articles: Built a text-embedding and LSTM model to extract reader sentiment from online political news articles.
  • Multi-Screen Data Visualization Platform: Designed and implemented a multi-screen data visualization platform using open-source software, contributing to significant Google funding.

Department of Electrical and Electronic Engineering, The University of Melbourne

Postdoctoral Researcher (Apr 2013-Dec 2013)

Conducted research on applying data science to biomedical applications.

  • Developed regression models to use in a novel electronic device to support home phototherapy management.
  • Validated the device and models against clinically approved phototherapy treatment devices used in healthcare settings.

The University of Melbourne

Graduate Researcher/PhD Student (Apr 2009-Mar 2013)

Conducted research on optimal content distribution strategies using stochastic modeling and mathematical optimization techniques.

  • Demostrated skills in Machine learning, Algorithm Design, and Optimisation
  • More than 20 publications in leading conferences and journals with 600+ citations (full list)

AT&T Labs Research, NJ, USA

Intern (Jul 2011-Oct 2011, Jul 2012-Oct 2012)

Conducted research on content delivery networks through large-scale data analysis.

  • Viewer Churn in On-Demand Video Services: Analyzed network traces using parametric models to understand viewer churn patterns.
  • Modeling User Interests in Video-On-Demand Services: Applied collaborative filtering to identify viewer preferences and optimize content distribution.

Education

The University of Melbourne, Australia

Ph.D in Electrical and Electronics Engineering (2009-2013)

  • Secured three highly competetive scholarships: Special Postgraduate Studentship, Melbourne Research Scholarship, and Overseas Research Experience Scholarship.
  • Research focused on machine learning, data analytics, probability theory, and mathematical modeling.
  • More than 20+ publications and a patent

The University of Peradeniya, Sri Lanka

B.Sc (Hons) in Electrical and Electronics Engineering (2002-2007)

  • Graduated top of a cohort of more than 320 students with a GPA of 4.0/4.0.
  • Ceylon Electricity Board Gold Medal and Prize for Best Performance in Electrical and Electronic Engineering
  • C.H Hewavitharana Prize for the Best Performance in Engineering: Awarded to the top-performing student across all disciplines of engineering.