A lot of data science roles talk about impact.
This one expects it.
You’ll be working on AI and GenAI powered features that sit directly in front of customers. Not internal dashboards. Not science projects. Actual product functionality where success is measured by whether it improves the experience and stands up in the real world.
This team is building ML and LLM driven capabilities into products used by thousands of customers. That brings real constraints, real trade offs, and real accountability. You’ll be working in domains where correctness, trust, and compliance matter as much as innovation.
They’ve moved past asking “can we do this?”.
Now the question is “is this the right thing to build, and how do we prove it works?”
What you’ll be doing
This is an end to end role. If you like owning problems rather than just parts of them, you’ll feel at home.
* Partnering closely with product managers to frame hypotheses and decide what’s worth building.
* Designing experiments and evaluation approaches that run through the full product lifecycle, not just at launch.
* Prototyping and validating GenAI and ML based solutions, then helping drive them through MVP and into production.
* Working across NLP, retrieval, forecasting, recommendations and anomaly detection, depending on the product area.
* Defining success metrics that matter to customers and the business, then using them to guide iteration.
* Running offline evaluations and online experiments to understand real customer and commercial impact.
* Working closely with ML Engineers and frontend teams to ship reliable, measurable and safe solutions.
* Operating comfortably in regulated, risk aware environments where models need to behave predictably.
* Sharing knowledge and influencing how data science is practiced beyond your own work.
You won’t be judged on how clever your model is. You’ll be judged on whether it improves the product.
Who this suits
You’ve shipped customer facing ML or GenAI features before. You’ve seen what happens when something goes live, and you understand how different that is from internal work.
Hands on GenAI experience is essential here. You’ve worked with LLM based systems, evaluation approaches, and the trade offs involved in deploying them to customers.
You bring a hypothesis driven mindset. You’re comfortable exploring complex systems, testing assumptions, and using evidence to guide decisions.
You’re technically solid. Strong Python, comfortable with data wrangling and modern ML tooling. You don’t need to be the strongest engineer in the room, but you need to be credible with those who are.
You operate as a senior. You influence peers, work across product, engineering, risk and legal, and communicate clearly when the subject matter is sensitive or ambiguous.
A PhD is welcome but not required. Evidence that your work has had impact beyond your own tasks matters more.
What’s in it for you
You’ll work on meaningful AI products with real users and real consequences, alongside strong ML Engineers and product leaders.
You’ll have room to shape how experimentation, evaluation and data science thinking show up in product decisions, not just in post hoc analysis.
Melbourne based team, with a preference for regular in person collaboration.
If you want a data science role where GenAI is real, experimentation matters, and your work ends up in customers’ hands, this is worth a conversation.
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