Data Solutions Analyst / Scientist

JOB TITLE: Data Solutions Analyst/Scientist
REPORTS TO: Manager, Network Reporting
LOCATION: Amman, Jordan
Working hours: Flexible to accommodate Australian time zone
About the opportunity
We’re seeking a Data Solutions Analyst / Scientist to lead forecasting, AI-driven models and scalable data products that support Australia Post’s national network operations. As part of the Network Insights, Analytics and Reporting team, you’ll own end-to-end network forecasting and partner with Operations, Planning, Finance, Commercial and Technology teams to deliver decision-ready insights. This high-impact role is ideal for someone who thrives in ambiguity, understands uncertainty, and can translate complex analytics into clear actions that improve service, cost, safety and network capacity outcomes.
About the role
You will lead the development and continuous improvement of forecasting solutions that support both operational execution and long-term network strategy.
Working with large, complex datasets, you’ll build and manage models that inform everything from daily planning decisions through to multi-year volume forecasts and network optimisation.
What you’ll deliver
Forecasting & strategic planning
Lead Network Operations end-to-end network forecasting across first, middle and last mile and interconnections across our Enterprise forecasting analytical teams
Enhance short and long-range volume forecasts so we can plan, execute and deliver beyond customer and commercial expectations
Build scenario models and sensitivity analysis to support planning under uncertainty
Integrate internal data with external drivers (economic, demographic, market trends)
Stakeholder partnership & decision influence
Partner with operations, planning and commercial teams to align forecasts to decisions
Translate complex outputs into clear, decision-ready insights and narratives
Influence trade-offs between cost, service, capacity and performance outcomes
Build trust in forecasts through transparency, governance and consistency
Advanced analytics & modelling
Apply statistical, machine learning and AI methods where appropriate, including time series forecasting, regression, causal modelling, anomaly detection, classification and clustering.
Continuously improve forecast accuracy, bias tracking and model performance
Determine when advanced modelling adds value vs simpler approaches
Data & analytics engineering
Design and build scalable, analytics-ready datasets supporting forecasting and planning
Develop production-grade data models and forecasting pipelines using SQL and cloud platforms such as GCP and BigQuery, with exposure to tools such as Dataform or Vertex AI highly regarded.
Apply analytics engineering practices (modelling, testing, CI/CD, version control)
Ensure strong data governance, quality and documentation across forecasting pipelines
Commercial impact & continuous improvement
Identify opportunities to improve cost efficiency, service outcomes and network performance
Translate insights into actionable changes across planning and operations
Champion a data-driven culture and continuous improvement
About you
5+ years’ experience in forecasting, data science or advanced analytics (ideally within logistics, supply chain or operations)
Strong experience building forecasting models using time series, regression or machine learning techniques
Experience working with large, complex, multi-source datasets
Willingness to learn and apply and showcase a challenging mind to improving forecasts and how they can be used in the business for commercial and service improvements
Advanced SQL capability and experience with cloud platforms (e.g. GCP, BigQuery)
Experience with Python/R or forecasting tools (e.g. Prophet, ARIMA, XGBoost)
Strong stakeholder engagement skills, with the ability to translate complex concepts into clear insights
Experience with analytics engineering practices (testing, Git, CI/CD workflows)
Data visualisation and storytelling capability (e.g. Tableau, Looker)
Commercial acumen and an understanding of cost, service and capacity trade-offs

Employment type
Full-Time

Work arrangement
No