AI Data Scientist
About the role
SolStak is hiring an AI Data Scientist to develop and deploy machine learning capabilities for secure, mission-ready analytics. You will work on sovereign AI solutions that support operational decision-making in Defence and government contexts.
What you’ll do
- Design, train and evaluate ML models for classification, forecasting and NLP use cases
- Build reproducible data science pipelines and MLOps workflows
- Collaborate with platform engineers to deploy models in accredited environments
- Apply responsible AI practices including bias testing and model explainability
- Translate complex analytical outputs into actionable insights for stakeholders
What we’re looking for
- Strong background in statistics, machine learning and Python data science stacks
- Experience with feature engineering, model validation and production deployment
- Curiosity, rigour and a passion for sovereign innovation
- Australian citizenship
- Minimum two years of proven experience in a relevant role
- Own GitLab instance and home lab environment (highly desirable)
- Existing Australian Government security clearance (highly desirable)
- Industry certifications (highly desirable)
What it feels like to work here
AI and data science at SolStak isn’t theoretical — your models support operational decision-making in accredited environments. You’ll work with clean, mission-relevant datasets, partner closely with platform engineers on deployment, and see your work move from notebook to production. The culture rewards intellectual rigour and practical delivery in equal measure.
Why engineers join
- Build sovereign AI capabilities for Defence and government missions
- Work on real MLOps pipelines in secure, accredited environments
- Collaborate with cloud, data and cyber teams on end-to-end AI programmes
- Apply responsible AI practices with national security context
- Hybrid flexibility across VIC, NSW and ACT with a research-minded team
What technologies you’re exposed to
Data scientists at SolStak work across the full technology stack — from raw data platforms through to cloud deployment, virtualisation and security controls.
Data engineering
- Cloudera, Spark, Hive, feature stores and enterprise data pipelines
- BigQuery, cloud data warehouses and large-scale analytics platforms
Cloud & hyperscalers
- AWS SageMaker, Azure ML, Vertex AI and sovereign cloud AI services
- GPU compute, model serving and scalable inference infrastructure
Virtualisation & platform
- Kubernetes model deployment, OpenShift and containerised ML workloads
- Python, TensorFlow, PyTorch, MLflow and MLOps tooling
Security products
- Secure data handling, classification and privacy-preserving analytics
- Model governance, audit trails and integration with enterprise security tooling