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 E nhance 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