AI Data Scientist

SolStak is hiring an AI Data Scientist to develop and deploy machine learning capabilitiesfor secure, mission-ready analytics. You will work on sovereign AI solutions that supportoperational 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
• 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 operationaldecision-making in accredited environments. You'll work with clean, mission-relevantdatasets, partner closely with platform engineers on deployment, and see your work move fromnotebook to production. The culture rewards intellectual rigour and practical delivery in equalmeasure.

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 platformsthrough to cloud deployment, virtualisation and security controls.

• Cloudera, Spark, Hive, feature stores and enterprise data pipelines
• BigQuery, cloud data warehouses and large-scale analytics platforms
• AWS SageMaker, Azure ML, Vertex AI and sovereign cloud AI services
• GPU compute, model serving and scalable inference infrastructure
• 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