openashbyhqqedinvestors
Senior Credit Risk Data Scientist
Wayflyer
LocationLondon
WorkplaceHybrid
EmploymentFullTime
Posted2026-03-13T12:31:07.921+00:00
Last observed2026-06-13 05:23:54.814834
Job idqedinvestors-wayflyer:ashbyhq:384acf8f-8762-4351-aa31-d957e92ce71e
π Company Mission Our mission is to give the world access to the best products by empowering great businesses to reach their growth potential. πΌ 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 https://wayflyer.com/our-customers/true-classic, Little Words Project https://wayflyer.com/our-customers/little-words-project and Kekoa Foods https://wayflyer.com/our-customers/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 https://www.youtube.com/watch?v=J4jcVcdIZ5g&ab_channel=Wayflyer to hear directly from them. π 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 https://wayflyer.com/careers. π 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 Wayflyerβs Quantitative Risk Team. We don't just build risk models; we own the process and are responsible for itβs 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. π¨ 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...
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