Senior Data Scientist

Wagetap is a profitable, 15-person startup on a mission to elevate people’s finances.

Today, over 50% of Australians live paycheck to paycheck without access to savings to smooth out the inevitable bumps life throws at us. Millions turn to unsustainable short-term solutions like payday loans, consumer credit, and credit cards just to get by.

Wagetap gives people early access to up to $2,000 of their wages, helping them avoid debt cycles and regain control of their finances.

We’ve lent over $1B to Australians, supported 250,000+ customers across 3.5M loans, and maintain a 4.9 rating on the App Store. The business is real, the product is live, and the next phase is about scaling globally without breaking what already works.

The Role

As a Senior Data Scientist, you’ll work on the systems that directly determine how we lend — who we lend to, how much, and how we manage risk. This is a highly applied role. You’ll be improving credit models, running experiments, and investigating real-world behaviour - not working on theoretical problems. Data Scientists at Wagetap enjoy tackling a wide variety of problems with high impact - from revenue, risk, to our ability to expand into new markets.

What you’ll do

• Build, improve, and extend credit and behavioural models
• Design and run experiments to optimise model performance and customer outcomes
• Investigate anomalies in customer behaviour, repayment patterns, and fraud
• Work closely with engineering to productionise and monitor models
• Contribute to business insights, forecasting, and decision-making

What we’re looking for

• Strong experience applying data science to real-world problems (likely 5+ years)
• Ability to balance model performance with commercial outcomes
• Experience working with production models and monitoring performance over time
• Experience in credit, lending, or risk with high volume behavioural data would be a plus
• Clear communication - you can explain trade-offs and decisions clearly
• Strong Python and SQL skills
• Intellectual range - you’re curious, quick to learn, and comfortable going deep on problems you haven’t seen before.
• Experience with ML techniques (e.g. regression, tree-based models)
• Familiarity with AWS data tools (S3, Glue, SageMaker)
• Nice to haves include experience in fintech, startups, or high-trust data environments

What else you need to know

• High ownership - engineers here make decisions, not just execute
• A profitable company with global ambition
• Competitive salary + real equity
• Sydney and Melbourne hubs (hybrid) with fully remote available
• Small, cohesive, and high-trust team