About Wayflyer
Today's small businesses need a capital provider that keeps pace with their growth ambitions. Traditional financing options are slow, cumbersome and often out of reach. That's why we built Wayflyer. Our technology allows us to assess businesses in minutes, generate financing offers that reflect their growth potential and send funds in as little as 24 hours. To date, we've deployed over $6bn to thousands of businesses worldwide, backed by Tier 1 banks like J.P. Morgan. You'll be collaborating with ambitious colleagues from around the world. We have offices in Dublin, London, New York, Charlotte, Berlin and Sydney.
The challenge
Wayflyer has deployed over $6bn in funding and we're not slowing down. Every credit decision we make is a bet on a business's real trajectory, and the models behind those bets need to get sharper and faster than they were yesterday. We're building the next generation of commercial credit, with AI running through every layer of the stack.
What you'll actually do
You'll own the full lifecycle of our credit risk models. We treat Machine Learning as a branch of software engineering here, so you'll write production-grade code, not just notebooks.
- Lead the end-to-end build of credit risk models, treating ML as software engineering so everything you ship is fast, reproducible and robust.
- Design modelling frameworks for decisioning, pricing and fraud that move company-wide P&L, not just a dashboard metric.
- Build credit policies and lending strategies that expand our addressable market safely, pushing conversion and profitability up together.
Scientific integrity is non-negotiable. Model improvements need to be statistically significant, with no data leakage and no bias hiding in the numbers. You'll turn technical findings into business trade-offs leadership can act on, mapping ROC AUC straight to revenue and loss, and you'll set the bar for code quality: reviewing, mentoring, and contributing to the libraries the whole team relies on. You'll stand out if you've worked on highly automated lending, price sensitivity modelling, or Customer Lifetime Value.
What this role could turn into
Senior Data Scientists here tend to grow into Staff or leadership roles with broader influence over risk strategy and team direction. We're building a quantitative risk function that genuinely drives P&L. The people who shape it early will have significant influence over where it goes.
The good stuff
25 days off, plus public holidays. Private healthcare, life insurance, and a pension. Equity, because you should own a piece of what you're building. Generous parental leave for primary and secondary caregivers. 60 days a year to work abroad from wherever you want.
Who thrives here
- Gets restless when a model is "good enough" but not great
- 4+ years building and maintaining production-grade ML systems
- Cares as much about code quality as the statistics behind it
- Strong grip on predictive modeling and credit risk concepts (IV, ROC AUC, SHAP, PD, EAD, LGD, EL)
- As comfortable in a modern monorepo (Python, SQL, Snowflake, dbt, and the rest of the stack) as a software engineer would be
- Would rather walk a commercial stakeholder through a ROC AUC curve and make them care about the revenue impact than hide behind jargon
- Uses AI as an accelerant, not a crutch
- Comfortable owning outcomes, not just tickets
We hire for range You'll need real depth on credit risk concepts (PD, EAD, LGD), a solid grasp of modelling techniques like IV and SHAP, and comfort working across Snowflake, dbt, ZenML, Dagster and Weights & Biases. Experience in automated lending, price sensitivity modelling or CLV is a strong plus.
