The Data Fellowship is an exclusive, competitive program to provide advanced data training to high performing Australian Public Service (APS) data specialists.
The programm consists of three month placements for up to ten APS officers a year, in which successful candidates will be taken offline to develop a solution for their nominated data-related problem or opportunity (which may include data analytics, foresighting, API development, etc). Data61 will scope and determine the most suitable placements, within Data61 or at an appropriate partner organisation.
After successful completion of the placement, Data Fellows will attend an achievement ceremony with the Secretary of the Department of the Prime Minister and Cabinet. Data Fellows will become part of the alumni cohort for future Data Fellows.
Applying for the program
The Data Fellowship program is not currently accepting applications. However, there are two application rounds each year:
Applications open in April for a July start:
- Applications open 19 April 2017
- Applications close 19 May 2017
- Candidates notified of results by 19 June 2017
- Candidates begin their placements from July 2017, pending consultation with Data61 and home agencies.
Applications open in September for a February 2018 start:
- Applications open 20 September 2017
- Applications close 20 October 2017
- Candidates notified of results by 20 November 2017
- Candidates begin their placements from February 2017, pending consultation with Data61 and home agencies.
Placements will run for approximately three months.
Commonwealth agencies and departments are invited to nominate high performing candidates via their Data Champion representative. For candidates in agencies without Data Champions, candidates should seek the support of a Senior Executive.
Candidates will be well placed to influence data skills and capability development in their organisation, and to disseminate the expertise and knowledge gained from their Data Fellowship placement throughout their organisation.
Agencies are invited to nominate up to ten candidates each application round.
Candidates should obtain support from their immediate supervisor, and ensure they are available to be taken offline to begin their placement within 3 months from the application close date .
Home agencies will continue to pay Data Fellows their regular salary, superannuation and entitlements.
Data Fellows’ travel and accommodation costs will be reimbursed by Data61 with funds apportioned by the Department of the Prime Minister and Cabinet.
Data61 will consult Data Fellows about their preferred location or placement organisation. The majority of Data Fellows are likely to be placed in the Data61 office closest to their current location. Data61 has offices located in most major cities in Australia and also operates in several regional locations.
Should Data61 and the Data Fellow agree that a partner organisation would be a more suitable venue for the placement, Data61 will broker the placement. However, preferences are not guaranteed.
Ten Data Fellows have undertaken the program so far:
- Alex Kelly (The Treasury)
Develop a new indicator for household consumption that provides a real-time read on household spending.
- Yingsong Hu (Department of Finance)
Build expenditure models for evidence-based policy design using a variety of health-related datasets.
- Audrey Lobo-Pulo (The Treasury)
Use machine-learning techniques to forecast real GDP growth in Australia and to compare the accuracy of this modelling approach to methods that are currently used.
- Janis Dalins (Australian Federal Police)
Leverage security-cleared researchers and developers to expand a real-time online file identification system supporting digital forensics, using fuzzy hashing for similarity detection and machine learning for predictive analysis through lightweight features such as metadata.
- Thomas Rutherford (Bureau of Infrastructure, Transport and Regional Economics)
Develop an agent-based model of a single container terminal, with a view to later scale up to the wider intermodal supply chain (e.g. multiple terminals, inland ports, key freight routes). Existing vessel and trade data will be used to generate synthetic inputs and to validate outputs of the model.
- Dominic Love (Australian Bureau of Statistics)
Use machine learning to streamline the compilation and production of the Australian Industry publication, including improvements to the processing cycle of the Economic Activity Survey.
- Imaina Widago (Department of Health)
Develop a microsimulation model for hospitalisation risk in chronic disease patients.
- Senani Karunaratne (Department of the Environment and Energy)
Develop an empirical model using machine learning algorithms with the aim to predict the changes of terrestrial soil carbon. This model is intended for use as a validation tool for the official estimates of greenhouse gas emissions from changes in soil carbon in Australia’s crop and grass lands.
- Tariq Scherer (Australian Securities and Investments Commission)
- Develop harmful trading detection techniques using ASIC data to identify patterns of repeated misconduct or relationships between entities of interest.
- Richard Green (Bureau of Infrastructure, Transport and Regional Economics)
Analyse large-scale transport data, including over 19 million GPS observations from over 200 freight vehicles, to assist government, the broader community and industry develop insights into the following areas: congested areas of the road network, rest patterns of truck drivers, and changes in road freight activity.
For further information about the Data Fellowship projects, please contact email@example.com and we will put you in touch with the project owner.