Lead Data Scientist
Lead Data Scientist contracting via AAR Consulting Group into a large Australian federal government department, leading end-to-end delivery of AI/ML solutions such as generative-AI knowledge search, predictive analytics and agentic AI. Core stack: Python, SAS, Teradata, Cloudera ML and Red Hat OpenShift; Australian citizens with (or eligible for) Baseline clearance.
- Compensation
- A$1.1K–A$1.3K / day
- Location
- Canberra ACT
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data science
- Posted
Skills
Must have
- Experienced with SAS, Teradata, Python, Cloudera Machine Learning (CML) and Red Hat OpenShift platforms
- Advanced degree in computer science, statistics, mathematics or related field with focus on data science, ML or AI
- Experience in data science with a proven track record
- Data science, analytical and modelling techniques including AI/ML applied to complex business problems
- Knowledge of data modelling techniques (relational, dimensional, messaging, big data), data analysis/profiling and visualisation
- Experience in statistical concepts, machine learning algorithms and modelling approaches on complex datasets
- Experience across data science platforms, analytical environments and integration approaches for scalable, reproducible delivery
- Experience applying best-practice data cleansing, validation, QA and feature preparation for complex datasets
- Must hold Baseline Security Clearance or be able to attain it (must be an Australian citizen)
Nice to have
- Awareness of industry best practice and trends
Full description
Salary: $1,100 - $1,300 per day (inclusive of super) neg.
This role can be based in Canberra, Brisbane, Adelaide or Hobart
Our client is a large, federal government department that is at the frontline of government service delivery - it’s using cutting edge technology to build world class platforms and capabilities
There are a range of projects including generative AI applications (knowledge search, intelligent information retrieval), predictive analytics and machine learning solutions that forecast outcomes and assess risk using historical data, and agentic AI capabilities
Role Description:
The Lead Data Scientist will lead the application of analytical, statistical and computational methods, including artificial intelligence, leveraging structured and unstructured data to supplement operational workflows and inform strategic decision making, across the end to end model lifecycle
Responsibilities:
- Lead and design the implementation of artificial intelligence and machine learning solutions that optimise for grounding, latency and cost
- Lead data acquisition, integration, transformation and feature engineering for complex datasets
- Design and implement robust qualitative and quantitative evaluation frameworks
- Lead the deployment, integration, monitoring and continual improvement of analytical, artificial intelligence and machine learning solutions across the model lifecycle
- Lead advanced analysis to identify trends, drivers and actionable insights
- Define modelling approaches and select appropriate statistical and machine learning techniques for complex problems
- Engage stakeholders to understand business needs & shape analytical solutions
- Lead and maintain documentation and ensure compliance with governance
- Provide quality assurance reviews
- Lead and manage team operations
- Evolve artificial intelligence, machine learning, statistical modelling and analytical techniques; and their application to complex business problems and datasets
Skills and Experience:
- Experienced with tools and technologies: SAS, Teradata, Python, Cloudera Machine Learning (CML) and Red Hat OpenShift platforms
- Advanced degree in computer science, statistics, mathematics, or a related field, with a focus on data science, machine learning, or artificial intelligence
- Experience in data science with a proven track record
- Data science, analytical and modelling techniques - including artificial intelligence and machine learning and their application to complex business problems
- Knowledge of data modelling techniques (relational, dimensional, messaging and big data), data analysis and profiling using modern analytical tools and programming languages, and visualisation methods and tools