Estimated start date
Tuesday, 26 August 2025
Initial contract duration
ASAP – 30 June 2026
Extension term
12 months
Number of extensions
1
Experience level
APS6 equivalent
Location of work
ACT, NSW, QLD, SA, VIC
Working arrangements
Hybrid
Maximum hours
40 hours per week
Security clearance
Must be able to obtain Negative Vetting Level 1
Job details
The Python Developer is responsible for:
Designing, developing, and deploying end-to-end data science solutions within the Microsoft Azure and Databricks environment. This role will involve working with large datasets stored in Azure Data Lake Storage (ADLS) Gen2 and Databricks, building and training machine learning models using Python and relevant libraries, and implementing automated pipelines for model deployment and operationalization. The ideal candidate will be a self-starter with a strong understanding of the data science lifecycle, capable of translating business problems into technical solutions and effectively collaborating with cross-functional teams.
The candidate must hold an active Baseline security clearance prior to commencement.
Key duties and responsibilities
The Python Developer will be responsible for, but not limited to:
- Develop, test, and maintain Python-based applications, scripts, and tools.
- Develop and implement robust Python-based solutions to efficiently read, process, and transform large datasets from Azure Data Lake Storage (ADLS) Gen2, Synapse and Databricks environments, ensuring data quality and readiness for model development.
- Design, implement, and train machine learning models using relevant Python libraries (e.g., scikit-learn, TensorFlow, PyTorch, MLflow within Databricks) to address specific business problems, iterating on model architecture and hyperparameters to achieve optimal performance.
- Develop and implement automated pipelines and deployment strategies (e.g., using Databricks Model Serving, Azure Machine Learning, containerisation) to seamlessly integrate trained models into production environments, ensuring scalability and reliability.
- Design and build automated workflows using Python and Azure services (e.g., Azure Data Factory, Databricks Workflows) to streamline data ingestion, model training, evaluation, and deployment processes, ensuring efficiency and repeatability.
- Implement monitoring solutions to track model performance and data drift in production, perform regular model evaluation, and develop strategies for model retraining and maintenance to ensure continued accuracy and relevance.
- Effectively collaborate with data engineers, business analysts, and other stakeholders to understand business requirements, communicate technical findings, and contribute to the overall data science strategy.
- Adhere to coding best practices, including version control, code documentation, and testing, to ensure maintainable, scalable, and high-quality Python code.
Applicants located outside of Canberra will be required to travel to Canberra for operational reasons as directed (e.g. onboarding, planning exercises [1-2 times per quarter], in person training, etc.). Any required travel will be discussed in advance and notice given wherever practicable.
Criteria
The buyer has specified that each candidate must provide a one page pitch to address all criteria specified. This is equal to 5000 characters.
Essential criteria
1. Proficiency in Python and its frameworks (e.g., Django, Flask, FastAPI).
2. Solid proficiency in Python programming and relevant data science libraries (e.g., Pandas, NumPy, scikit-learn).
3. Demonstrable experience building, training, and evaluating machine learning models using frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
4. Strong problem-solving and analytical skills with the ability to translate business requirements into technical solutions.
5. Experience with RESTful APIs and microservices architecture.
6. Ability to handle and respect the sensitivities of datasets and the use of Personally Identifiable Information (PII)
Desirable criteria
1. Familiarity with DevOps practices and CI/CD pipelines.
2. Experience with Databricks, including using Spark for data processing and MLflow for model management.
3. Experience with at least one model deployment framework or service (e.g., Databricks Model Serving, Azure Machine Learning.
4. Bachelor’s or Master’s degree in a relevant field (e.g., Computer Science, Data Science, Statistics, Engineering).
