Senior Credit Risk Data Scientist
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At a glance
- Employment type
- Full-time
- Workplace
- Hybrid
- Category
- Data engineering
Technologies & skills
What you'll be doing
Senior Credit Risk Data Scientist at Wayflyer: Own the full lifecycle of credit risk models, building production-grade ML systems for commercial lending. Collaborate globally, drive P&L impact, and set high standards for code and statistical integrity. Hybrid role in Dublin or London.
- Lead end-to-end build of credit risk models as production-grade software
- Design modelling frameworks for decisioning, pricing, and fraud
- Build credit policies and lending strategies to expand market safely
- Ensure scientific integrity and statistical significance in model improvements
- Translate technical findings into actionable business trade-offs
- Review, mentor, and contribute to team libraries
Key requirements
Must-have
- 4+ years building and maintaining production-grade ML systems
- Strong grip on predictive modeling and credit risk concepts (IV, ROC AUC, SHAP, PD, EAD, LGD, EL)
- Comfort working with Python, SQL, Snowflake, dbt
- Focus on code quality and statistical rigor
- Ability to communicate technical findings to commercial stakeholders
Nice-to-have
- Experience in automated lending
- Experience in price sensitivity modelling
- Experience in Customer Lifetime Value (CLV)
Experience: 4+ years building and maintaining production-grade ML systems
Role signals
- Technical focus
- credit risk modeling, machine learning, software engineering
- Leadership
- Mentoring
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
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.
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Lead the end-to-end build of credit risk models, treating ML as software engineering so everything you ship is fast, reproducible and robust.
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Design modelling frameworks for decisioning, pricing and fraud that move company-wide P&L, not just a dashboard metric.
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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.
Who thrives here
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Gets restless when a model is "good enough" but not great
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4+ years building and maintaining production-grade ML systems
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Cares as much about code quality as the statistics behind it
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Strong grip on predictive modeling and credit risk concepts (IV, ROC AUC, SHAP, PD, EAD, LGD, EL)
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As comfortable in a modern monorepo (Python, SQL, Snowflake, dbt, and the rest of the stack) as a software engineer would be
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Would rather walk a commercial stakeholder through a ROC AUC curve and make them care about the revenue impact than hide behind jargon
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Uses AI as an accelerant, not a crutch
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.
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.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Senior Credit Risk Data Scientist role at Wayflyer focuses on leading the end-to-end development of production-grade credit risk models, ensuring statistical rigor, and translating technical insights into business impact within a fast-paced, AI-driven lending environment.
- Credit risk modeling expertise·Medium
- Production ML engineering·High
- Business communication·High
- Mentoring and code review·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Credit Risk Modeling Concepts
0–7 minRefresh your understanding of PD, EAD, LGD, IV, ROC AUC, SHAP, and their application in commercial lending.
Prepare STAR Stories for End-to-End Model Delivery
7–14 minDraft concise examples of projects where you led model development, ensured statistical rigor, and communicated results to stakeholders.
Brush Up on Production ML Engineering Practices
14–20 minReview best practices for code quality, reproducibility, and deployment using Python, SQL, and tools like Snowflake and dbt.
Research Wayflyer and Their Approach to Lending
20–25 minRead available materials to understand Wayflyer's business model, technology stack, and recent innovations.
Prepare Questions and Practice Communication
25–30 minFormulate thoughtful questions for the interviewers and practice explaining technical concepts in business terms.
Talking points
, 5 itemsEnd-to-End Credit Risk Model Development
Be ready to discuss your experience owning the full lifecycle of credit risk models, from data ingestion to deployment, as this is central to the role.
Statistical Integrity and Model Validation
Prepare examples where you ensured statistical significance, avoided data leakage, and validated models using metrics like ROC AUC and IV.
Production-Grade ML Engineering
Demonstrate your ability to write robust, reproducible code in Python and SQL, and your familiarity with tools like Snowflake and dbt.
Translating Technical Results to Business Impact
Show how you have communicated complex model outputs to non-technical stakeholders and influenced business decisions.
Mentoring and Code Review
Highlight your experience mentoring others, reviewing code, and contributing to shared libraries or frameworks.
What to research
, 4 itemsWayflyer's Credit Risk Modeling Approach
Research Wayflyer's public materials, blog posts, or case studies to understand their approach to credit risk and lending innovation.
Production ML Engineering Best Practices
Review best practices for deploying, monitoring, and maintaining ML models in production, especially in financial services.
Key Credit Risk Metrics and Techniques
Refresh your knowledge of IV, ROC AUC, SHAP, PD, EAD, LGD, and EL, and how they are applied in commercial lending.
Modern Data Stack Tools
Familiarize yourself with Python, SQL, Snowflake, dbt, and any related orchestration or ML ops tools mentioned.
Questions to ask
, 6 itemsHow does Wayflyer balance rapid innovation in credit modeling with the need for robust risk controls?
Why ask this? Shows your awareness of the trade-offs between speed and risk in fintech.
What are the biggest technical or business challenges currently facing the credit risk data science team?
Why ask this? Helps you understand the team's pain points and where you can add value.
How is success measured for credit risk models at Wayflyer beyond traditional metrics?
Why ask this? Clarifies how your work will be evaluated and what outcomes matter most.
What opportunities exist for mentoring or influencing the broader data science function?
Why ask this? Assesses the scope for leadership and professional growth.
How does the team ensure scientific integrity and avoid bias in model development?
Why ask this? Demonstrates your commitment to ethical and rigorous modeling.
What is the typical workflow for deploying and monitoring new models in production?
Why ask this? Gives insight into the team's engineering maturity and operational practices.
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