About AARNet
Australia’s Academic and Research Network (AARNet) was established in 1989 and is widely regarded as the founder of the Internet in Australia and renowned as the architect, builder and operator of world-class network infrastructure for research and education.
We are Australia’s National Research and Education Network (NREN). We connect over one million users—researchers, faculty, staff and students—at institutions across Australia, supporting education and research across a diverse range disciplines including high energy physics, climate science, genomics, radio astronomy and the arts.
Nationally, AARNet interconnects Australian universities, the CSIRO, and other organisations who have a research and education mission, or with whom the education and research sector interacts. These include hospitals, vocational training providers, schools and museums. Internationally, AARNet interconnects the Australian Research and Education (R & E) community to the world – and continuously develops new capabilities and partnerships to facilitate seamless data access and transfer.
AARNet also offers a suite of supporting applications to our customers. These include network and collaboration services such as CloudStor and Zoom, that enable innovation in the delivery of research and education.
We are an organisation of innovators, doers, and courageous thinkers. We are not constrained by traditional products and solutions and we constantly strive to build the solutions that our customers will need tomorrow – today. If you have the imagination, foresight and drive to build the future why not come and join us?
The Role
We are looking for a passionate data scientist to streamline identification of security threats using data analytics. The ideal candidate will have a keen interest in the application of big data analytics and machine learning techniques to important and complex real world data sets.
More specifically at AARNet we will be applying machines learning and data science techniques to the field of cyber security using enterprise network data including but not limited to user/machine authentication data, user/resource access logs, and other machine generated data to detect advanced security threats.
Responsibilities
- Collaborate with security domain experts, data scientists, and platform engineering team to identify security threats with data-driven methods
- Perform data processing and transformation to maximize informational value
- Conduct research activities including idea proposition, literature review, fast algorithm and learning model prototyping, experiments running, and documentation
- Implement performance-efficient models and algorithms for production
- Be responsible for accuracy and performance of models in production. Review and improve where necessary.
- Work under general guidance with minimal close supervision
- Communicate and demonstrate success of data science-based methodologies for internal and external use.
Expertise, experience & qualifications
- M.S. or Ph.D. in a relevant technical field with machine learning and data mining, or 4+ years experience in a relevant role. Particular areas of interests include semi or unsupervised learning, anomaly detection, graph or network analysis.
- Extensive experience solving analytical problems using quantitative approaches
- Comfort in manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources
- Ability to investigate an ill-defined data problem and crystalize it to an appropriate learning framework
- Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner
- Familiarity with databases, preferably noSQL databases
- Expert knowledge of an analysis tool such as R, Matlab, Python
- Experience working with large data sets, experience working with distributed computing a plus (Map/Reduce, Hadoop, Hive, Apache Spark, etc.)
- Good production coding skills in Java or Scala
- Familiarity with machine generated data is a plus
Conditions of employment
AARNet is committed to diversity and providing equal opportunity to all. We’re a great place to work if you want to make a difference. Remuneration will be based on skills and experience and will include an above market superannuation package.