Senior Credit Risk Data Scientist

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 Every funding decision Wayflyer makes is a bet on a business, on its trajectory, on whether the numbers tell the real story. We've deployed over $6bn and we're scaling fast. The models that underpin those decisions need to be sharper, faster, and more commercially precise than they were yesterday. We're building the next generation of commercial credit business and using AI across every layer of the stack. What you'll actually do 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. 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. Who thrives here 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. You can walk a non-technical stakeholder through a ROC AUC curve and make them care about what it means for revenue. How You’ll Stand Out Experience in highly automated lending and portfolio optimisation Experience in price setting and modelling price sensitivity Experience in modelling Customer Lifetime Value (CLV) We hire for range 4 or more years in Data Science or ML, with a track record of building and maintaining production-grade systems. Advanced Python and SQL are essential. You'll need deep knowledge of credit risk concepts; PD, EAD & LGD, a good grasp on ML modelling concepts like IV & SHAP, and be comfortable working across tools like Snowflake, dbt, ZenML, Dagster, and Weights & Biases. Experience with highly automated lending, price sensitivity modelling, or Customer Lifetime Value is a strong plus. The ability to leverage AI and LLMs to accelerate your own output matters too. Location and working policy 📍 Dublin HQ or London, hybrid. 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. By submitting your application, you acknowledge that Wayflyer Limited will process your personal data for the purpose of evaluating your suitability for the role. Such processing is based on the need to take steps prior to entering into a potential employment agreement. To learn more about how we handle your personal data, you can contact our privacy team at privacy@wayflyer.com or review our privacy notice at https://wayflyer.com/privacy-notice .