Data Scientist

Application closing date: Wednesday, 10 April 2024 • 11:59pm, Canberra time

Estimated start date: Wednesday, 01 May 2024

Location of work: SA

Working arrangements: Flexible work arrangements available

Length of contract: 12 months

Contract extensions: 2 x 12 months

Security clearance: Must be able to obtain Baseline

Rates: $120 - $150 per hour (inc. super)

The Biosecurity Analytics Centre, Digital Business Division, requires skilled data scientists to work in a high performing data warehousing team delivering analytics products to support business outcomes.

The team member will provide services under direction including:

• Follow the principles defined¬ in the team's Data Science Blueprint

• Define and formulate data-related problems and objectives in collaboration with domain experts and business stakeholders.

• Collaborate with data engineers who will provide data curation services.

• Explore and analyse data using descriptive and inferential statistics, data visualization, and hypothesis testing.

• Apply and implement machine learning and deep learning models, such as regression, classification, clustering and natural language processing

• Optimise and evaluate model performance using appropriate metrics and techniques, such as cross-validation, grid search, or A/B testing.

• Deploy and maintain data pipelines and machine learning models in production environments.

• Communicate and present data analysis and machine learning results to technical and non-technical audiences using clear and concise reports, dashboards, or slides.

• Write clear, business meaningful, code documentation that explains the intent and mechanics of scripts.

The staff will be part of a team that is:

• Business-focused – highest priority is given to considering the business problem and ensuring the technical work is solving real-world problems.

• Code-first – highest technical rigor in code is demanded, expectations are clearly defined and to be met.

• Multi-disciplinary – team members are to be considerate of how staff from other professions contribute to the overall outcome, and collaborate accordingly.

• Open – code, communication, documentations, peer-review are open-by-default, including early drafts of work.

Essential Criteria

• Data science – ability to identify opportunities that would benefit from rigorous application of statistical and machine learning techniques (e.g. regression, classification, clustering, natural language processing, computer vision, or recommender systems) and implement these solutions in maintainable Python/R code in established frameworks (e.g. pandas, numpy, scikit-learn, TensorFlow, PyTorch, or Spark). The data scientist aspires to guide the multidisciplinary team in applying quantitative sciences to real-world business problems. They do this by being engaged in all aspects of the problem life-cycle from business analysis, problem definition, development, code productionisation and post-production monitoring. They are committed to "working backwards" from the business needs. Rather than starting with an a priori commitment to a particular algorithm, they begin with preliminary exploration and continuously re-evaluate hypothesis in an iterative process with the team until business needs are met. (Response Weighting 34%)

• Data analysis - ability to derive business intelligence through SQL, Power BI or Python exploration of curated business data, and communicate these persuasively through data visualisation. The developer aspires to be the multidisciplinary team's forerunner to delivering value by formulating and answering business questions as realistic data problems. They do this by proactively understanding the business process, the lineage and quality of the data capture, and the strategic intent of the business. (Response Weighting 33%)

• Team player – ability to work independently with limited guidance to create self-direction in uncertain and rapidly changing environments, displaying the characteristics of creativity and resilience. Ability to work openly and collegiately in a multi-disciplinary team, displaying a genuine consideration of how members from other disciplines contribute to the overall outcome. They have a strong focus on people and business outcomes over formal methodologies. (Response Weighting 33%)

Desirable Criteria

• Relevant technologies – Databricks, Azure Data Lake, Azure SQL Server.

• Familiarity with the Australia's biosecurity systems. Familiarity with the imported cargo pathway will be highly regarded.

• Familiarity with the Australia's exports systems.