* Day Rate - $800 - $900 Inclusive of superannuation
* Hybrid Working Arrangements - 50% WFH
* 12 Months Contract
About the Organisation
Client is a large public sector organisation with a strong focus on data‐driven decision making, public safety, and evidence‐based policy development. Through the Chief Data Office, Client delivers advanced analytics, data science, and AI capabilities to support high‐impact programs and strategic initiatives across the organisation.
The Opportunity
The Senior Data Scientist is a senior technical specialist within the Chief Data Office, responsible for leading complex, high‐impact data science and advanced analytics initiatives across the organisation. This role provides deep expertise in advanced statistics, machine learning, and modern AI, translating complex analytical problems into end‐to‐end, production‐ready solutions that inform operational, tactical, and strategic decision‐making. The position owns the full lifecycle of data science initiatives, from problem definition and hypothesis formulation through to model development, implementation, testing, validation, monitoring, and communication of outcomes.
Responsibilities
Reporting to the Data Science Manager, the Senior Data Scientist acts as a trusted technical expert and advisor, delivering statistically rigorous and scientifically defensible solutions. The role also involves mentoring analysts and data scientists, while supporting non‐technical stakeholders by translating complex technical concepts into clear, practical, and business‐focused insights.
Roles and Responsibilities includes:
* Lead the design, development, and delivery of advanced statistical and machine learning solutions, including regression, classification, time‐series analysis, predictive forecasting, Bayesian methods, random forest modelling, causal inference, and hypothesis testing
* Design and advise on robust experimental and statistical methodologies, including considerations around statistical power, confidence intervals, prediction intervals, bias, assumptions, and data limitations
* Apply supervised and unsupervised machine learning techniques, ensemble methods, feature engineering, model optimisation, and robust evaluation frameworks
* Design and implement deep learning solutions where appropriate, including CNNs, RNNs, and transformer‐based architectures
* Own and document the end‐to‐end machine learning lifecycle, including data ingestion, preparation, modelling, validation, deployment, monitoring, and continuous improvement
* Ensure analytical solutions are statistically sound, explainable, reproducible, and aligned with ethical, privacy, confidentiality, security, and regulatory requirements
* Work closely with business stakeholders to clearly define analytical problems, success criteria, and business objectives
* Develop structured, well‐documented analytical approaches that clearly describe reasoning, assumptions, and decision logic
* Implement and rigorously test solutions, validating outcomes against business and technical performance measures
* Provide clear, evidence‐based recommendations to support operational, tactical, and strategic decision‐making
* Demonstrate expert‐level Python programming using libraries such as NumPy, pandas, scikit‐learn, PyTorch and/or TensorFlow
* Write efficient, maintainable SQL to analyse large and complex datasets
* Contribute to scalable and maintainable analytics and ML codebases, applying best practices in version control, testing, documentation, and reproducibility
* Collaborate with data engineering, platform, and architecture teams to support production deployment and monitoring of models
* Act as a trusted advisor to senior stakeholders, effectively translating technical concepts into plain English for non‐technical audiences
* Mentor analysts and junior data scientists, providing technical guidance and support
* Communicate complex analytical insights through concise briefings, reports, visualisations, and presentations
* Guide stakeholders on appropriate use of data science and AI, managing expectations around feasibility, risk, benefits, and limitations
Required Skills
* 8–10 years' industry experience delivering advanced statistics, data science, machine learning, and applied research solutions in complex environments
* Tertiary qualification in Data Science, Mathematics, Statistics, Physics, Computer Science, or a related quantitative discipline
* Master's or Doctoral qualification (PhD), or significant applied research experience, highly desirable
* Demonstrated expert‐level knowledge of advanced statistics, modelling, inference, and experimental design
* Strong experience in supervised and unsupervised machine learning, feature engineering, model evaluation, and optimisation
* Practical experience with modern AI techniques, including exposure to deep learning (e.g. computer vision, NLP, transformers)
* Proven ability to deliver end‐to‐end data science solutions from problem definition through to implementation and validation
* Mandatory expert‐level Python skills (NumPy, pandas, scikit‐learn, PyTorch/TensorFlow) and extremely strong SQL capability
* Experience working with large, complex, and sensitive datasets in highly secure environments
* Strong ability to explain complex technical concepts to non‐technical stakeholders
* Ability to obtain and maintain required security clearances
Desired Skills
* Experience delivering analytics or AI solutions within public sector or highly regulated environments
* Exposure to productionising machine learning models at scale
* Strong understanding of ethical AI, governance, and responsible data science practices
How To Apply
If you're ready to take on this exciting opportunity and join Client, please apply here. Alternatively, you can email Seema Jabeen at seema.jabeen@teksystems.com, quoting Job Reference J N -052026-38902 to register your interest.
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