Company Mission
Our mission is to give the world access to the best products by empowering great businesses to reach their growth potential.
About Wayflyer
Today's SMBs 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 unique 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.
Since launching in April 2020, we've deployed over $5bn to thousands of businesses worldwide, backed by Tier 1 banks like J.P. Morgan. We've become a trusted financing partner for some amazing brands, like True Classic, Little Words Project and Kekoa Foods.
Teams at Wayflyer are truly cross-functional. You'll be collaborating with ambitious colleagues from around the world - all striving to deliver on a huge opportunity. Check out this video to hear directly from them.
Culture & Values at Wayflyer
At Wayflyer, we value being sound people, excellent operators, and ambitious overachievers, working together with integrity, creativity, and bold optimism to deliver exceptional results. To learn more, please visit our website.
Your Role at a Glance: The key ways you'll bring value to the team
As a Senior Credit Risk Data Scientist, you will be a key driver within Wayflyer’s Quantitative Risk Team. We don't just build risk models; we own the process and are responsible for it’s impact on our portfolio performance.
In this role, you will bridge the gap between complex statistical theory and tangible financial outcomes. You will lead strategic initiatives turn data into dollars, scaling our global funding operations to drive consistent revenue growth and improve our bottom line.
We treat Machine Learning as a branch of software engineering, meaning you will be responsible for writing production-grade code that powers our underwriting systems.
- Production-Grade ML Engineering: Lead the end-to-end lifecycle of credit risk models, treating Machine Learning as a branch of software engineering to ensure all outputs are fast, reproducible, and robust.
- Strategic Risk Modelling: Design and implement sophisticated modeling frameworks (such as decisioning, pricing or fraud methodologies) that move the needle on company-wide P&L.
- Credit Strategy & Optimisation: Develop data-driven credit policies and lending strategies that safely expand our addressable market, driving higher conversion and maximising our growth and profitability.
- Statistical Rigour: Serve as a guardian of scientific integrity, ensuring that model improvements are statistically significant and free from data leakage or bias.
- Commercial Influence: Translate technical findings into "Business Trade-offs" for leadership, mapping model performance (like ROC AUC) directly to revenue and loss metrics.
- Technical Stewardship: Set the standard for code quality within the team by performing high-value code reviews, mentoring junior team members, and contributing to core internal libraries.
🌴 Time Off That Matters Recharge with 25 days of paid annual leave, plus public holidays.
🧘 Your Wellbeing Access support and resources to support your wellbeing, including our Wellbeing @ Wayflyer ERG.
🫀 Comprehensive Health & Protection Stay covered with private healthcare, life insurance, and critical illness cover.
👵 Secure Your Future with Our Pension Plan Our pension plan helps you build a strong foundation for tomorrow, starting today.
🍼 Family-First Policies We provide a generous parental and adoptive leave program for both Primary and Secondary caregivers, ensuring you have the dedicated, fully paid time you need to focus on your family.
💰 Share in Our Success With our equity scheme, you're not just an employee; you're a stakeholder in our journey.
🌴 Work From Abroad! Enjoy the freedom to work remotely from anywhere in the world for up to 60 calendar days a year.
Global Offices and Working Policy
🇮🇪 Dublin, Our Buzzing HQ Our headquarters operates a hybrid model. The number of in-office days depends on your team, but the majority of teams and our SLT are typically in 3-4 days per week, giving you valuable exposure to senior leadership.
🇬🇧 London, One Big Family Home to around 30 employees, our brand-new London office also runs on a hybrid model. You’ll often find our CEO working alongside the team with regular visits.
🇦🇺 Sydney, Small but Mighty Our close-knit Sydney office has around 20 employees and follows a flexible hybrid model, fostering strong collaboration.
🇺🇸 Charlotte, our new US-HQ
Charlotte is our US headquarters and the foundation of our North American growth. From our Uptown office, our teams collaborate closely, build relationships and drive impact across the region, with a setup designed for both focus and flexibility.
🌍 Remote, Across the US & Europe The majority of our Tech org works remotely across the UK and Europe, coming together throughout the year for off-sites, company kick off and technical kick off. We also have a growing number of remote team members across the US.
What Makes You a Great Fit
- Experience: 4+ years in Data Science/ML, with a proven track record of building and maintaining production-grade ML systems.
- Statistical & Modeling Depth: Advanced knowledge of predictive modeling and credit risk concepts (e.g., IV, ROC AUC, SHAP).
- Engineering Excellence: Advanced proficiency in Python and SQL. You should possess the core skills of a Software Engineer, comfortable working in a modern monorepo and using tools like Snowflake, dbt, ZenML, Dagster, Weights & Biases, etc.
- AI Native: Ability to leverage AI and LLMs as accelerants for high-quality output.
- Domain Expertise: Deep understanding of credit risk metrics (PD, EAD, LGD, EL) and the trade-offs between risk appetite and growth.
- Communication Skills: The ability to persuade non-technical stakeholders and distill high-level risk strategy into a clear, actionable story.
- Experience in highly automated lending and portfolio optimisation
- Experience in price setting and modelling price sensitivity
- Experience in modelling Customer Lifetime Value (CLV)
