Australia’s data fellowship program for public service staff open for application
To give talented Australian Public Service staff an opportunity to develop advanced data skills, data fellowship offered by Australian Government is now open for application.
According to the announcement made by the Digital Transformation Agency (DTA), the data fellowship programme by the Australian Government targets current employees of the Australian Public Service who are interested in building data skills within their agencies.
Up to 10 high-performing data specialists in the Australian Public Service will be selected to develop a solution for a data-related problem or opportunity.
The data fellowship is a 3-month full-time placement with no cost to data fellows. Agencies will continue to pay their salaries, superannuation and entitlements, with all travel and accommodation costs reimbursed.
Before their application, applicants should make sure that they have approval from the Data Champion or a senior executive of their agencies and are able to start the project with 3 months of the application closing date in March.
Data Champions are senior officials within Government agencies tasked with promoting data use, sharing and reuse within their organisations.
Data fellows will work with Data61 or another partner organisation during their placement on projects involving data analysis, forecasting or API development. The DTA and Data61 will consult with the data fellow’s agency on the start date. With offices in most major cities in Australia as well as some regional locations, most data fellows will be placed in the Data61 office closest to their current locations.
Other than training on advanced data skills, data fellow will also be part of a bigger alumni network after completing the placement to share learnings and experience.
Some of the previous projects that data fellow alumni worked on include:
(1) Using social media resources and data on trends such as travel, retail, home and car sales, to provide a new real-time indicator of household consumption and spending
(2) Using health-related data sets to create expenditure models for evidence-based policy design
(3) Applying machine learning techniques to compare different GDP modelling in Australia, so as to create a new way of predicting economic growth in Australia.
(4) Applying machine learning techniques to conduct predictive analysis based on lightweight feature, such as metadata, to expanding a real-time file identification system that supports digital forensics
(5) Using vessel and trade data to design an agent-based model of a container terminal model for data analysis. The model will later scale up to the wider intermodal supply chain.
(6) Using machine learning to streamline how the Australian Industry publication is compiled and produced. This included improvements to the processing cycle of the Economic Activity Survey.
(7) Designing a microsimulation model to conduct simulation for health risk and assess hospitalisation risk in chronic disease patients.
(8) Building an empirical model to estimate greenhouse gas emissions and predict the changes of terrestrial soil carbon. By monitoring soil carbon in Australia’s crop and grasslands, this model can be used as a validation tool for the official estimates of greenhouse gas emissions.
(9) Developing techniques to detect harmful trading detection techniques using data from the Australian Securities and Investments Commission. These techniques find patterns of repeated misconduct and relationships between entities of interest.
(10) Building a process and platform that analyses GPS data from road freight vehicles. This provided insights into congested areas of the road network, rest patterns of truck drivers, and changes in road freight activity.