Job Description
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Own the technical architecture and end-to-end delivery of each workstream, from data foundations through models and optimisation to evidence proving effectiveness
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Serve as the technical face of the engagement
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Run methodology reviews, steering sessions, and executive readouts for client leaders and partners
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Lead a pod of machine‐learning and data engineers
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Set standards for code review, testing, reproducibility, and documentation
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Build and operate production data pipelines on Google Cloud Platform and BigQuery at large scale across billions of rows
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Apply cost discipline and data‐governance controls
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Design, ship, and validate propensity, uplift, visitation‐frequency, and offer‐adoption models
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Apply causal inference in production, including uplift modelling, matched‐control difference‐in‐differences, double machine learning, holdout and experiment design, and power analysis
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Build optimisation engines using mixed‐integer and linear programming, assignment methods, and scheduling methods
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Maintain architecture documents, decision registers, and evidence packs suitable for adversarial technical review
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Translate technical work into plain language for business and executive audiences
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Collaborate with client business and technical teams and advisory and consulting partners
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Requirements
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Ten or more years building and shipping production data and machine‐learning systems, including time spent leading technically or leading a team
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Deep, hands‐on Python and SQL
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Production data engineering on a major cloud platform
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Applied causal inference and experimentation, including uplift modelling, difference‐in‐differences, double machine learning, and power analysis
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Mathematical optimisation applied to operational problems, including mixed‐integer or linear programming and assignment or scheduling problems
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Track record of client‐facing delivery and presenting to senior executives and external delivery partners
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Experience building agentic workflows and integrating LLMs and agentic systems into production solutions
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Production‐grade testing, reproducibility, data lineage, and documentation
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Comfort operating in regulated, high‐governance data environments
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Right to work in Australia, or a base in a time zone within about two hours of Australian Eastern Time
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Resume and application materials must be submitted in English
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Melbourne preferred; strong candidates elsewhere in Australia considered
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Experience with Google Cloud Platform and BigQuery ideal; AWS, Databricks, or Microsoft Azure experience transfers well
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Domain experience in gaming, casino, hospitality, or loyalty and marketing analytics is a nice‐to‐have
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Experience with recommendation systems,



marketing and offer optimisation, or workforce and roster optimisation is a nice‐to‐have
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Experience integrating vendor systems for yield management, workforce management, or player and table tracking is a nice‐to‐have
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Background in responsible‐gaming, anti‐money‐laundering, or privacy‐by‐design is a nice‐to‐have
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Consulting or client‐embedded delivery experience is a nice‐to‐have
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Core Competencies
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Demonstrates expertise in building and delivering production data and machine‐learning systems, with a robust focus on applied causal inference, mathematical optimization, and client‐facing delivery. Proficient in managing technical architecture and collaborating with cross‐functional teams in regulated data environments.
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Highest‐signal resume keywords
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Python
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SQL
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Google Cloud Platform
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BigQuery
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Causal Inference
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Hard Skills
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Machine Learning
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Data Engineering
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Mathematical Optimization
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Production Data Pipelines
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Uplift Modelling
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Mixed‐Integer Programming
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Linear Programming
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Testing and Reproducibility
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Data Governance
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Documentation
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Soft Skills
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Client‐Facing Delivery
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Communication
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Leadership
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Industry Keywords
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Gaming
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Casino
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Hospitality
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Loyalty Analytics
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Marketing Analytics
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Tools & Technologies
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AWS
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Databricks
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Microsoft Azure
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📌 Principal Ai Engineer - Technical Lead (Victoria)
🏢 Jobtailor
📍 Victoria