Company overview:
Blue Orange Digital is a boutique data & AI consultancy that delivers enterprise-grade results. We design and build modern data platforms, analytics, and ML/AI Agent solutions for mid‑market and enterprise clients across Private Equity, Financial Services, Healthcare, and Retail.
Our teams work with technologies like Databricks, Snowflake, dbt, and the broader Microsoft ecosystem to turn messy, real-world data into trustworthy, actionable insight.
We’re a builder‑led, client‑first culture that prizes ownership, clear communication, and shipping high‑impact work.
About the Role
We are building AI systems for a flagship Australian client in the regulated gaming and entertainment sector. The work began as a set of proofs of concept and is now moving into piloted production, with further workstreams expected as the engagement grows. You would be the technical owner: the person who designs these systems, builds them, and stands in front of the people who depend on them.
The projects span the applied artificial intelligence stack. Some are machine-learning recommendation and decision systems that determine who to engage, what to offer, and how to act, grounded in causal evidence rather than guesswork. Others are forecasting and optimization systems that make operations and workforce planning sharper and more responsive. As the practice grows, expect the work to reach further into modern artificial intelligence, including generative and agentic approaches, wherever it earns its place. Across all of it, we build production-grade systems on cloud data platforms, with governance, privacy, and responsible-use controls designed in from the start.
You would own the architecture and delivery of this work, lead a pod of engineers, and be the technical face of the engagement. You will collaborate closely with the client's business and technical teams and with the advisory and consulting partners they bring alongside, translating deeply technical work into plain language and holding the standard under scrutiny. This is a hands-on principal role. You will write code, set the technical standard, and own the room.
Level: Principal
Location: Melbourne preferred. We will consider strong candidates based elsewhere in Australia, and exceptional candidates located in a time zone within about two hours of Australian Eastern Time. The role carries regular on-site presence at client locations.
Employment: Full-time
Service Line: Artificial Intelligence and Machine Learning Implementation
Own the technical architecture and end-to-end delivery of each workstream, from data foundations through models and optimisation to the evidence that proves they work.
Serve as the technical face of the engagement, running methodology reviews, steering sessions, and executive readouts for the client's leaders and the partners they work with.
Lead a pod of machine-learning and data engineers, and set the standard 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, with strict cost discipline and data-governance controls.
Design, ship, and validate machine-learning models, including propensity, uplift, visitation frequency, and offer-adoption models.
Apply causal inference in production, including uplift modelling, difference-in-differences with matched controls, double machine learning, holdout and experiment design, and the power analysis that sizes what a pilot can detect.
Build optimisation engines using mixed-integer and linear programming, along with assignment and scheduling methods.
Maintain architecture documents, decision registers, and evidence packs that hold up to adversarial technical review, and translate the work into plain language for business and executive audiences.
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, and production data engineering on a major cloud platform.
Google Cloud Platform and BigQuery are ideal; Amazon Web Services, Databricks, or Microsoft Azure experience transfers well.
Applied causal inference and experimentation, not prediction alone: uplift modelling, difference-in differences, double machine learning, and power analysis.
Mathematical optimisation applied to real operational problems, including mixed-integer or linear programming and assignment or scheduling problems.
A track record of client-facing delivery, with the presence to own a room of senior executives and external delivery partners.
Experience with building agentic workflows and integrating LLMs and agentic systems into production solutions.
Production-grade standards as a working habit: testing, reproducibility, data lineage, and documentation.
Comfort operating in a regulated, high-governance data environment where controls and auditability matter.
The right to work in Australia, or a base in a time zone within about two hours of Australian Eastern Time.
Domain experience in gaming, casino, hospitality, or loyalty and marketing analytics.
Experience with recommendation systems, marketing and offer optimisation, or workforce and roster optimisation.
Experience integrating vendor systems for yield management, workforce management, or player and table tracking.
A background in responsible-gaming, anti-money-laundering, or privacy-by-design.
Consulting or client-embedded delivery experience.
A lead role on a high-visibility engagement, with direct access to client executives and senior stakeholders.
The chance to be the founding technical presence for Blue Orange Digital in Australia.
Work that spans the full breadth of applied artificial intelligence, from data engineering through causal inference to mathematical optimisation, rather than one narrow slice.
A builder culture where engineers lead and ship, and where technical judgement carries weight.
Competitive compensation with performance-based bonus.
Professional development budget and certification support.
Flexible, hybrid working arrangements around the on-site needs of the engagement.
Note: Please submit your resume in English, as all application materials must be in English for review and consideration.
