Some data scientists want the cleanest possible dataset. Some want to spend months perfecting a model.
And then there are those who want to see something they discovered on Monday become a product experiment by Friday.
A stealth payments startup is building the monetisation platform for modern software companies.
The goal is to make it faster and easier for software businesses to change pricing, launch new business models, expand internationally, improve payment performance and create better buying and upgrade experiences.
The founders spent the better part of a decade building monetisation systems inside a global software firm. They saw what becomes possible when infrastructure, product thinking and commercial data work together. They also saw how difficult it is for most companies to recreate that capability themselves.
Now they’re turning that experience into a product.
This isn’t another payment processor. It’s a platform that sits across billing and payments, helping software companies support subscriptions, usage, credits, seats, tiers and hybrid models without stitching together fragmented systems or building everything in-house.
A central part of the vision is an intelligence layer that can tell customers what they should do next.
That might mean localising a price in a particular market, changing how a payment is routed, identifying a cohort ready for a packaging change or recommending an experiment to improve conversion or retention.
They’re hiring their first Product Data Scientist to help build it.
Operating as a product builder: finding the right question, working through imperfect evidence and turning the answer into something customers can understand and use.
You won’t be sitting downstream, building dashboards and answering questions after decisions have already been made.
You’ll report to the CTO and work directly with the founding team, product, design, engineering and early design partners.
You’ll help decide which problems the company should solve, what information the product needs and how raw monetisation data becomes a recommendation a customer can understand and act on.
You’ll shape the question, define the data, analyse the opportunity, develop a recommendation, design the experiment and help turn the answer into a product experience.
Day one won’t come with a giant proprietary dataset or a mature data function.
You’ll need to work out how to deliver useful intelligence to early customers using individual merchant data, external benchmarks, market signals, thoughtful rules and tested assumptions.
You’ll also build the foundations underneath it. That includes creating reliable visibility into merchant usage, health and adoption, and structuring billing and payment data so it can flow into the intelligence layer.
As the customer base grows, you’ll help evolve that intelligence from rules and heuristics into more adaptive recommendations. Eventually, these systems will be able to recommend, test or act on a customer’s behalf.
That ambiguity is part of the job.
You might investigate why paid retention is under performing in a new market, whether a local payment method would improve conversion, which customers should see a different package, or how payment routing should respond to performance, cost, geography and payment-method signals.
The goal isn’t technical sophistication for its own sake. It is giving software businesses credible evidence they can use to make better commercial decisions.
You’ll care about statistical rigour because customers will make real decisions based on your work. You’ll care about data quality because weak evidence creates weak recommendations. And you’ll care about product design because even the best analysis is useless if nobody understands or trusts it.
You’ll be:
- Owning monetisation and product analysis from the initial question through to a shipped recommendation
- Building the data foundations behind both internal decision-making and the customer-facing intelligence layer
- Identifying opportunities across pricing, packaging, payments, conversion and retention
- Building early product intelligence using merchant data, rules, heuristics and external benchmarks
- Developing the data strategy and roadmap from rules-based intelligence toward adaptive and agentic systems
- Setting the standard for how an early-stage company uses data to make product and commercial decisions
During the first 6-12 months, success will mean:
- Giving the team reliable, self-serve visibility into merchant usage, health and adoption
- Helping merchants bring their billing and payment data into the platform in a structured and reliable way
- Taking at least three pricing, packaging or payment opportunities through to a real experiment or customer change, each with a measurable before-and-after result
- Delivering the agreed data strategy and roadmap for the intelligence layer
Ideal experience:
- Senior, hands-on experience in product data science, growth data science or applied data science
- Strong statistical foundations, including experimentation, hypothesis testing and working with imperfect evidence
- Experience using behavioural and commercial data to influence a product roadmap
- Payment’s experience is strongly preferred. Product-led growth or B2C software experience is the strongest alternative
The important thing is that you enjoy building data products as much as analysing them.
This is a founding-stage team with real design partners, direct customer exposure and a lot of consequential work still sitting in blank space. Early hires won’t inherit a finished operating manual. They’ll help shape the product, culture and way the company works.
The team is based in Surry Hills and aims to spend around three days together in the office each week.
Interview process will include a technical assessment and a case study.
The package range is $150k-$190k base + super + ESOP.
Apply through the link or contact ronny@theonset.com.au.