Data Solutions Analyst / Scientist

Job Summary

This role leads forecasting, AI-driven models, and scalable data products to support Australia Post’s national network operations. It involves end-to-end network forecasting and collaboration with multiple teams to provide actionable insights.

Responsibilities

• Lead comprehensive network forecasting across first, middle, last mile, and interconnections within Enterprise forecasting teams.
• Improve short- and long-term volume forecasts to exceed customer and commercial expectations.
• Create scenario models and conduct sensitivity analyses to aid planning under uncertainty.
• Combine internal data with external factors like economic, demographic, and market trends.
• Collaborate with operations, planning, and commercial teams to align forecasts with decision-making.
• Communicate complex analytical results as clear, actionable insights and narratives.
• Use statistical, machine learning, and AI methods including time series forecasting, regression, causal modelling, anomaly detection, classification, and clustering.
• Design and build scalable datasets and production-grade data models and forecasting pipelines using SQL and cloud platforms such as GCP and BigQuery.

Requirements

• 5+ years of experience in forecasting, data science, or advanced analytics, preferably in logistics, supply chain, or operations.
• Proficient in building forecasting models using time series, regression, or machine learning techniques.
• Experience handling large, complex, multi-source datasets.
• Advanced SQL skills and experience with cloud platforms like GCP and BigQuery.
• Knowledge of Python/R or forecasting tools such as Prophet, ARIMA, or XGBoost.
• Strong stakeholder engagement skills with the ability to simplify complex concepts.
• Familiarity with analytics engineering practices including testing, Git, and CI/CD workflows.
• Capability in data visualization and storytelling using tools like Tableau or Looker.
• Understanding of commercial factors including cost, service, and capacity trade-offs.

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