Updating Results

Australian Bureau Of Statistics (ABS)

4.3
  • #4 in Government & public services
  • 1,000 - 50,000 employees

What will I do as an Australian Government Data Graduate?

In 2021 the Australian Government is recruiting over 150 graduates for roles working with data.

Data roles are varied and support evidence-based, informed decision making, and work across all aspects of government such as policy development, research, program management and service delivery. Roles cover the full data lifecycle including survey and questionnaire development, research, data acquisition, data engineering and data analysis, as well as more specialised streams such as data science, methodology, geospatial analysis and data management. 

We recruit graduates for three streams: Data Analyst, Data Scientist and Statistical Methodologist.

Data Analysts

Data Analysts analyse data to discover relationships between data items and draw findings, using their judgement and real-world insights. They tell the stories behind data in an engaging and meaningful way including through data visualisation.

They may be responsible for analysing and interpreting survey, census and administrative data including linked data, assist in designing and testing data collection instruments such as questionnaires and forms, and undertaking quality assurance of data. 

Other types of roles in this stream may include Data Acquisition Officers who are responsible for the ethical collection of data, and Data Consultants who are responsible for engaging with clients and delivering their data needs safely and effectively.

Desirable fields of study: There are no requirements for any particular degrees or disciplines, though people who have undertaken data subjects are more likely to be successful. The types of backgrounds our graduates are from include analytics, actuarial studies, data science or mining, demography, economics, econometrics, engineering, finance, mathematics or mathematical modelling, machine learning, psychology, risk and intelligence analysis, social sciences and statistics.

Data Scientists

Data Scientists are responsible for discovering, experimenting with, wrangling data and leading the use of new data sources, methods and products to meet the changing information needs of customers across governments, business and the community. Data Scientists play an important role in building their organisation’s capacity to take advantage of opportunities, such as advances in technology, big data, and use of administrative information for statistical purposes.

As a Data Scientist you may have skills in data mining, machine learning, and other techniques such as regression, prediction, clustering, time series analysis, association rules, sequence analysis, visualisation and data manipulation. You would be expected to have some experience with statistical programming languages such as (but not limited to) SAS, Python and R.

Other types of roles in this stream may include Data Engineers who are responsible for reliable infrastructure for data, Data Managers who are responsible for effectively managing data as an enterprise asset, or Geospatial Analysts.

Desirable fields of study: computer science, mathematics, science, engineering or other disciplines with a strong quantitative background.

Statistical Methodologists

Statistical Methodologists provide authoritative statistical methodological advice to internal and external clients, and identify, research, implement and maintain a range of innovative statistical solutions. Methodologists support the high quality of their organisation’s statistical collection and production process by ensuring data collection methods are based on sound, defensible statistical principles, as well as being cost-effective.

Methodologists conduct research in data access, integration, confidentialisation and analysis methods for traditional and emerging data sources. They undertake research on statistical methods and aim to obtain robust statistics which minimise the load on data providers - through efficient sample designs and techniques to control sample overlap between surveys, or through the use of existing data sources to reduce the need for direct collection of data. They also have a substantial role in quality management, methods for survey processing (such as estimation, imputation and accuracy estimation), and time series analysis.

Desirable fields of study: statistics, mathematics, econometrics, cognitive science, behavioural science or other fields with a strong quantitative discipline.