Type: Full-time - Hybrid 1 day per week sydney office Build production AI models that power real workforce decisions, not research prototypes that never ship. You'll design and deploy forecasting and optimisation models that generate 170,000+ shifts for 20,000+ employees across major franchise networks. This isn't academic research - you're building the decision engine that handles multi-jurisdiction compliance, hourly demand shifts, and operational complexity at enterprise scale. You'll work with Databricks processing 2.2B+ records, using Python, PyTorch, and MLFlow to build models that actually get deployed. The team uses Claude, Cursor, and agentic workflows daily - AI tools aren't optional, they're expected to multiply your output. You'll ship working solutions rather than perfect ones, with clear 30/60/90 day milestones and measurable KPIs like forecast accuracy and auto-acceptance rates. This is a small team of around 5 engineers where every hire needs to multiply output. You'll own problem spaces end-to-end rather than narrow specialisations. The company punches above its weight with publicly-traded enterprise customers, but you won't get lost in corporate bureaucracy. Success is measured by what you shipped and what improved, not credentials or consensus-building. What You'll Do: Build time-series forecasting models for operations, demand, and traffic across multiple verticals Design optimisation algorithms handling deterministic, stochastic, and Bayesian problem classes Implement MLOps infrastructure including model registries, monitoring, and CI/CD pipelines Partner with product teams to translate business outcomes into measurable data science deliverables What You'll Need: Experience shipping production ML/DS systems with demonstrated business impact Strong Python and SQL skills, plus experience with Databricks or similar lakehouse systems Time-series forecasting experience and solid grounding in optimisation techniques (linear programming, heuristics, constraint programming) Proven ability to use AI coding tools systematically while maintaining rigour About the Company: They're building an AI decision agent for back-office operations, turning operational data into automated actions. Their customers include major franchise networks across thousands of locations who chose them because legacy systems can't handle the complexity and scale they operate at. Apply: Please click the 'Apply' button. Don't worry if your CV isn't up to date - just send what you have. #J-18808-Ljbffr