Job Description

Some data scientists want to publish papers. Some want to secure a FAANG logo on their CV.

And then there are the ones who want to be able to explain the work that they do simply at a dinner party. Even if they are building and shipping systems that millions of people use.

The hiring firm is a scale-up processing billions in financial transactions annually. Traditionally data science was seen as a support function - building dashboards, running analyses, supporting other teams’ decisions. But that’s changing.

They’re in the middle of a fundamental shift: data science is moving from analytics to product. These roles will own that shift in one of their highest-impact product areas. You’ll be building systems that help customers understand their decisions, size opportunities, and automate complex workflows. You’ll work on problems like conversion propensity modelling, opportunity sizing, and ML-driven product recommendations.

You’ll report to their Head of Data Science - someone who understands that great data science is about shipping things that matter, not perfect models that sit in notebooks. You’ll own projects end-to-end: from scoping through to customers using your work. And you’ll grow into a technical leader on a team that’s scaling.

This isn’t an analytics role. It’s a product role with deep technical foundation.

At traditional banks and large corporates, data science is often downstream - analytics that supports decisions made elsewhere. You build models that sit in notebooks. Impact is measured quarterly, if at all. Change takes months.

Here, data science is upstream. Your work shapes product direction. Your insights directly influence what millions of users see. An idea you propose on Tuesday can be tested with real users by Thursday. You care about measurement and rigour - not because someone’s forcing you to, but because good systems at scale are expensive and you want to be proud of what ships.

You’re working alongside engineers who care about code quality and deployment. Product managers who understand that measurement matters. A leader who’s been in the technical trenches and knows the difference between elegant theory and pragmatic shipping.

And you’re not alone. You’re part of a growing data science team. Learning from more senior practitioners. Building skills that compound. You’ll be:

• Owning product analytics projects end-to-end (scoping, analysis, experimentation, storytelling)
• Building ML models that inform product decisions and drive feature adoption
• Working hands-on with SQL and Python to explore data and validate hypotheses

Ideal experience:

• 3-5 years of hands-on data science or analytics experience
• Strong SQL and Python skills
• Experience with product analytics
• Statistical thinking - you design experiments, test hypotheses, know when to be sceptical
• Software engineering mindset - clean code, testing, and reproducibility matter to you

Melbourne or Sydney. Hybrid work (1-2 days/week onsite). You’re not commuting daily. You have focused time to code, but you’re in the room when decisions matter.

You’ll also get autonomy to own problems, fast iteration cycles, mentorship from experienced data scientists, and the rare opportunity to help shape how a company thinks about data science at scale.

Interested?

Contact ronny@theonset.com.au / 0448 808 848