What is a data fellowship?
A data fellowship is a 3 month full-time placement. We select high-performing data specialists in the Australian Public Service to develop a solution for a data-related problem or opportunity. The project may include activities like data analysis, forecasting or API development.
Data fellows will work with Data61 or another partner organisation during their placement.
Who is it for?
To be eligible, you must be employed by the Australian Public Service.
You should be interested in building data skills within your agency and be happy to share your knowledge and expertise with colleagues.
How to apply
You can apply online.
Applications open 26 February and close 23 March 2018. Up to 10 fellows will be chosen.
Before you apply, make sure:
- you have approval from your agency’s Data Champion or a senior executive
- you can start the project within 3 months of the application closing date.
The DTA and Data61 will consult with your agency to determine an appropriate start date. The placement runs for about 3 months.
In your application, you will need to submit:
- the name of your data champion or senior executive
- the name of your current supervisor
- your preferred placement start date and location
- details of your data related project proposal and technical skillset
- your up-to-date resume.
You can preview the form before you complete it.
There is no cost to data fellows.
Agencies will continue to pay your salary, superannuation and entitlements.
Any travel and accommodation costs will be reimbursed.
Data61 has 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 your current location.
You may also be placed with one of Data61’s partner organisations. This will be negotiated once you are selected and Data61 will arrange the placement. Placement with partner organisations is not guaranteed.
Past data fellows and projects
After completing your placement, data fellows become part of an alumni network. Below is a list of successful projects completed by our alumni.
Real-time household consumption
Alex Kelly (The Treasury)
A new way of measuring household spending using social media resources. Alex used data on trends including travel, retail, home and car sales to provide a real-time indicator of household consumption.
Expenditure models for policy design
Yingsong Hu (Department of Finance)
Using health-related data sets, Yingsong created expenditure models for evidence-based policy design.
Comparing different GDP modelling in Australia
Audrey Lobo-Pulo (The Treasury)
Audrey used machine learning techniques to create a new way of predicting economic growth in Australia.
Expanding a real-time file identification system
Janis Dalins (Australian Federal Police)
Janis expanded on a real-time file identification system that supports digital forensics. Machine learning allowed for predictive analysis based on lightweight features such as metadata. Similarity checks were done with fuzzy hashing.
Container terminal model for data analysis
Thomas Rutherford (Bureau of Infrastructure, Transport and Regional Economics)
Thomas designed an agent-based model of a single container terminal. This model uses vessel and trade data to create synthetic inputs and to validate outputs. This will later scale up to the wider intermodal supply chain.
Streamlining data publication
Dominic Love (Australian Bureau of Statistics)
Dominic used 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.
Simulation for health risk
Imaina Widagdo (Department of Health)
Imaina designed a microsimulation model to assess hospitalisation risk in chronic disease patients.
Estimating greenhouse gas emissions
Senani Karunaratne (Department of the Environment and Energy)
Senani developed an empirical model to 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.
Harmful trading detection techniques
Tariq Scherer (Australian Securities and Investments Commission)
Tariq used ASIC data to develop techniques for detecting harmful trading. These techniques find patterns of repeated misconduct and relationships between entities of interest.
Analysis of truck and road freight activity
Richard Green (Bureau of Infrastructure, Transport and Regional Economics)
Richard created a process and platform for analysing 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.
Get in touch
For more information about the data fellowship program or past projects please email firstname.lastname@example.org