Full Stack Data Scientist
At Pet Circle, we believe pets deserve the very best, and the people who love them deserve a better way to care for them. Since 2011, we've grown from that single idea into Australia's biggest online pet retailer, with around 500 Pet Circlers across Australia, New Zealand and the Philippines, reshaping how pet parents discover, choose and receive what their pets need.
Pet care is a $15 billion industry in Australia, and as pet ownership continues to grow, so do the expectations of modern pet parents. They want expert advice, personalised experiences, convenience and reliability, all delivered seamlessly. That's where we come in. We're building the future of pet care through technology, innovation and a deep understanding of what pets and their people need. It's a big opportunity, and we're only just scratching the surface.
The role
This is an applied full stack data science role partnering closely with the Customer team. You will not just design models. You will build and ship them. The work spans customer lifetime value, survival analysis, incrementality testing and channel mix: problems that are technically demanding and commercially consequential. You will work directly with marketing and commercial stakeholders to frame the right question, build the right model, and translate outputs into decisions.
This is a senior individual contributor role. Your influence is through technical excellence and thought leadership, not line management. The expectation is that you raise the bar for how data is used in Marketing decisions and, over time, help define what rigorous applied data science looks like at Pet Circle more broadly. You will partner closely with the Data Platform engineering team to get models into production and ensure outputs are surfaced reliably.
What you’ll be working on
Build and own lifetime value and churn models that drive how Marketing prioritises customer segments and allocates spend across acquisition and retention channels.
Design and run incrementality experiments, randomised controlled trials and quasi-experiments, that establish whether our marketing interventions actually cause the outcomes we attribute to them, and build the standards that govern how these experiments are set up, powered and interpreted.
Develop spend-curve and scenario planning models that translate channel investment into forecasted incremental outcomes, giving commercial and marketing teams the tools to make allocation decisions with confidence.
Work directly with marketing and commercial stakeholders to frame hypotheses, design clean tests and communicate results, including the uncertainty in them, in terms non-technical audiences can act on.
Productionising models, monitor for drift and maintain reliability over time, with a clear path from prototype to production.
What we're looking for
Ideal
7+ years in applied data science, statistical modelling or quantitative research, with a track record of shipping models that influenced real decisions, not just delivered analysis.
Builder mentality: you write production-quality Python or R, you care about getting things into the hands of decision-makers, and you know when a workable deployed model beats a perfect one still in development. You move fast without creating problems you cannot see.
Strong statistical modelling: regression, survival and hazard models, Bayesian inference, time series.
Comfortable with data platforms on AWS or GCP with an ability to deploy and scale batch jobs or always-on models.
Causal inference expertise: difference-in-differences, synthetic control, interrupted time series, matching methods.
Solid SQL and hands-on experience with a cloud data warehouse (BigQuery preferred).
The ability to communicate with non-technical stakeholders clearly enough that they can act on your outputs. Explaining the model is as important as building it.
AI-native ways of working: you use AI agents and tools as a genuine accelerator in your workflow, for analysis, code generation and data exploration. You evaluate model outputs critically, know when to push back, and are actively extending what you can do independently through agentic approaches. This is a core expectation of the role, not an optional extra.
A bonus
Experience in marketing analytics: customer LTV, media mix modelling or channel attribution in an e-commerce or subscription context.
MLOps experience: model versioning, deployment pipelines, monitoring and retraining workflows.
Familiarity with the GCP ecosystem (BigQuery, Vertex AI, Cloud Run).
Experience presenting to senior commercial stakeholders or embedding models into business planning cycles.
Background in e-commerce, subscription or retail where customer behaviour data is central to the business.
Where you'll work
This is a Sydney-based role. You will closely collaborate in-person with the Customer team. Our office is above Wynyard station, it's pet-friendly (of course), and we work flexibly, a mix of office and home, with the team typically in the office 3-4
A few good things
Heaps of development opportunities - lunch & learns, secondments & unlimited learning courses via Go1
Flexible working environment
Up to 50% employee discount
Product sample giveaways
Pawrental leave and wellbeing leave
Volunteer days for the causes you care about
Fitness corporate partnership
Why you'll want in
Looking after pets, and the pet parents who love them, is what gets us out of bed. That energy carries into how we work, and into what your days here would feel like.
You'll own your patch and have the room to act on it. We move fast and decide fast, and we'd rather test an idea and learn from it than sit on it. You'll have rich, real-time data at your fingertips to back your thinking, and the freedom to chase an opportunity the moment you spot one. The best idea wins, wherever it comes from.
What holds it together is what we stand for:
We're better together
We operate with humble hearts and hungry minds
Pets matter, so pets and pet parents always have a seat at our table
We're instinctively curious. Always