Own the technical architecture and end-to-end delivery of each workstream, from data foundations through models and optimisation to evidence proving effectiveness
Serve as the technical face of the engagement
Run methodology reviews, steering sessions, and executive readouts for client leaders and partners
Lead a pod of machine‐learning and data engineers
Set standards for code review, testing, reproducibility, and documentation
Build and operate production data pipelines on Google Cloud Platform and BigQuery at large scale across billions of rows
Apply cost discipline and data‐governance controls
Design, ship, and validate propensity, uplift, visitation‐frequency, and offer‐adoption models
Apply causal inference in production, including uplift modelling, matched‐control difference‐in‐differences, double machine learning, holdout and experiment design, and power analysis
Build optimisation engines using mixed‐integer and linear programming, assignment methods, and scheduling methods
Maintain architecture documents, decision registers, and evidence packs suitable for adversarial technical review
Translate technical work into plain language for business and executive audiences
Collaborate with client business and technical teams and advisory and consulting partners
Requirements
Ten or more years building and shipping production data and machine‐learning systems, including time spent leading technically or leading a team
Deep, hands‐on Python and SQL
Production data engineering on a major cloud platform
Applied causal inference and experimentation, including uplift modelling, difference‐in‐differences, double machine learning, and power analysis
Mathematical optimisation applied to operational problems, including mixed‐integer or linear programming and assignment or scheduling problems
Track record of client‐facing delivery and presenting to senior executives and external delivery partners




Experience building agentic workflows and integrating LLMs and agentic systems into production solutions
Production‐grade testing, reproducibility, data lineage, and documentation
Comfort operating in regulated, high‐governance data environments
Right to work in Australia, or a base in a time zone within about two hours of Australian Eastern Time
Resume and application materials must be submitted in English
Melbourne preferred; strong candidates elsewhere in Australia considered
Experience with Google Cloud Platform and BigQuery ideal; AWS, Databricks, or Microsoft Azure experience transfers well
Domain experience in gaming, casino, hospitality, or loyalty and marketing analytics is a nice‐to‐have
Experience with recommendation systems, marketing and offer optimisation, or workforce and roster optimisation is a nice‐to‐have
Experience integrating vendor systems for yield management, workforce management, or player and table tracking is a nice‐to‐have
Background in responsible‐gaming, anti‐money‐laundering, or privacy‐by‐design is a nice‐to‐have
Consulting or client‐embedded delivery experience is a nice‐to‐have
Core Competencies
Demonstrates expertise in building and delivering production data and machine‐learning systems, with a solid 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.
Highest‐signal resume keywords
Python
SQL
Google Cloud Platform
BigQuery
Causal Inference
Hard Skills
Machine Learning
Data Engineering
Mathematical Optimization
Production Data Pipelines
Uplift Modelling
Mixed‐Integer Programming
Linear Programming
Testing and Reproducibility
Data Governance
Documentation
Soft Skills
Client‐Facing Delivery
Communication
Leadership
Industry Keywords
Gaming
Casino
Hospitality
Loyalty Analytics
Marketing Analytics
Tools & Technologies
AWS
Databricks
Microsoft Azure
#J-*****-Ljbffr

📌 Principal Ai Engineer – Technical Lead (Melbourne)
🏢 Jobtailor
📍 Melbourne