Our client is one of Australia's largest hospitality groups, operating a national portfolio of pubs, bars and event venues. The business has grown largely through acquisition. Each venue is run with a local-first approach, tailored to its community rather than following a single template.
About the role
The business needs a demand forecasting model for the venues it has acquired in the last 12 months. You will own that build, from approach through to production. The core challenge is limited history. These venues have only a short trading record in the company's systems, and earlier data may sit in different point-of-sale systems or be incomplete. You will need to forecast reliably by drawing on patterns from comparable venues across the wider network, then improve the model as each site's own data builds up. Your forecasts will feed directly into how venues roster staff, order stock and plan trading.
Key responsibilities
Design and build a demand forecasting model for recently acquired venues, covering revenue, covers and product demand at daily and intraday levels
Solve the cold-start problem by using data from comparable venues, hierarchical approaches and pre-trained time series models where they help
Bring in external drivers such as weather, public holidays, school holidays, local events, sport and promotions
Clean, align and integrate venue data from different point-of-sale and operational systems
Deploy the model to production with automated retraining, monitoring and drift detection




Set transparent accuracy measures and report performance by venue, region and venue type
Work with operations, finance and venue managers so forecasts are understood, trusted and used
Document the approach so the model can be maintained and extended to future acquisitions
Essential skills and experience
5+ years in data science, with demand or sales forecasting models built and run in production
Strong Python and SQL
Hands-on experience with forecasting and ML methods such as LightGBM, XGBoost, Prophet or ARIMA
Experience forecasting with sparse, short or patchy history, such as new stores or sites
Experience deploying and monitoring models on a cloud platform (Azure, AWS or Databricks)
Ability to explain model outputs and limitations to non-technical stakeholders
Tertiary qualification in data science, statistics, mathematics, engineering or a related field
Desirable skills
Hospitality, retail, QSR or other multi-site business experience
Hierarchical forecasting (for example network, region, venue, product)
Experience with foundation time series models such as Chronos or TimeGPT
Databricks, PySpark and MLflow
Weather and event-driven demand modelling
Promotional uplift or causal impact analysis
Halcyon Knights is an equal opportunities employment agency, valuing the contributions made by all people and welcomes applications from people with disability, Aboriginal Australians, people of all ages and from culturally diverse backgrounds.
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📌 Senior Data Scientist (Melbourne)
🏢 Halcyon Knights
📍 Melbourne