Developing Procurement Classifications & Machine-Learning Modelling

Location: Brisbane CBD, QLD

Duration: 4 months

Proposed start date: July/August

Project Background

Confirm IT is the Queensland Government’s primary ICT services provider, delivering whole-of-government and agency-specific services. This project will see the intern located in Brisbane alongside a highly-experienced team of quantitative data and machine-learning specialists. The project will review, design and refine models that categorise Government’s procurement and transactional data.

Research to be Conducted

Investigate, research and document the complete development process including stages, milestones, timelines, estimated resources, and release date for a Minimum Viable Product (MVP) to achieve the project’s key objective of delivering a categorisation tool for the categorisation of Government’s procurement data. This will include:

  1. Review procurement data resources and analytics systems
  2. Determine a suite of acceptable success criteria for the model
  3. Develop the model based on a subset of available datasets and test validity
  4. Assess the quality of results and package the model into a deployable module

Skills Required

We are looking for a PhD student with the following:

ESSENTIAL

  • From a quantitative discipline such as: Mathematics, Statistics or Data Science.
  • Excellent Python programming skills
  • Sound knowledge of Python libraries and other similar related and available services

DESIRABLE

  • Experience with Microsoft Cognitive Services and/or Google TensorFlow
  • Prior experience in large-scale categorisation projects

Expected Outcomes

Outcomes will include:

  • A brief evaluation report
  • Error free, efficient and well documented code to implement models

Additional Details

The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.

It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner.  The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

Applications Close

 19 June 2019

Reference

APR – 1012