Founding Product Data Scientist

Some data scientists want the cleanest possible dataset. Some want to spend months perfecting a model.

And there some 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 them to change pricing, launch new business models, expand internationally, improve 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.

You won’t be sitting downstream, building dashboards and answering questions after decisions have already been made.

You’ll work alongside product, design and engineering to 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.

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 underperforming 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.

You’ll need good technical foundations, but the goal isn’t technical sophistication above all else.

It’s all about giving software businesses enough credible evidence to make better commercial decisions.

You’ll care about data 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
• Exploring data, testing hypotheses and measuring results
• Identifying opportunities across pricing, packaging, payments, conversion and retention
• Designing experiments and defining how success will be measured
• Building early product intelligence using merchant data, rules, heuristics and external benchmarks
• Setting the standard for how an early-stage company uses data to make product and commercial decisions

Ideal experience

• Strong hands-on experience in product data science, growth analytics or product analytics
• Experience using behavioural and commercial data to influence a product roadmap
• Exposure to pricing, subscriptions, payments, conversion, retention or product-led growth
• The ability to explain complex analysis clearly to product, engineering, commercial and customer audiences
• Comfort working from first principles when the data, infrastructure and answer aren’t already there

You could currently be a Senior Product Data Scientist, Growth Data Scientist, Product Analyst or commercially minded Analytics Engineer. The important thing is that you enjoy building data products as much as you do analyse 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. Process is kept light, with focused time to work and regular opportunities to make decisions together.

Interested?

Apply through the link or [email protected]