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.

Despite processing billions in financial transactions annually, for years; data science was seen as a support function - building dashboards, running analyses for “the business”, supporting decisions – not really making them. But that's changing.

They're in the middle of a fundamental shift: data science is moving from analytics to critical in driving product design. They're building intelligent agents that customers depend on daily. They're embedding sophisticated systems into core workflows. They're investing heavily in measurement and governance because they believe good data science requires both rigor and speed.

You'll report to their new Director of Data Science - someone who sits at the table where strategic product and engineering decisions happen. You'll own initiatives end-to-end: from conception through to customers using your work. You'll mentor early-career data scientists across several teams - a technical leadership role, not a people-management one. And you'll help them figure out what it means to be a truly data-driven organisation.

At traditional 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.

And critically: you're not the only data scientist being relied upon. Mentoring matters here because the function is scaling across several teams - you'll raise the technical bar by coaching peers and early-career data scientists.

Ideal experience:

• 7-9 years of hands-on data science experience
• Strong Python and SQL
• Experience shipping models to production (SDLC mindset)
• Comfort with LLMs, modern ML infrastructure
• Ability to work independently and own outcomes end-to-end
• Track record of raising the technical bar through mentoring and peer influence — not necessarily through formal leadership

Melbourne or Sydney. Hybrid work (min 1-2 days/week onsite). You’ll 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, a team culture that values both rigor and speed, and the rare opportunity to help shape how a company thinks about data science at scale.

Apply through the link or contact ronny@theonset.com.au / 0448 808 848