Ramp
Senior
Own credit risk models end-to-end at a fintech scaling $200B+ in annualized spend
Ramp is hiring a Senior Applied Scientist to build the ML models behind its credit risk decisioning and portfolio management. You'll own the full lifecycle from data exploration to production monitoring, working at the intersection of ML, causal inference, and economics. This interview probes both modeling depth and the judgment to ship reliable models that move real risk decisions.
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What this interview tests
- Credit risk model design and portfolio decisioning
- Full applied science lifecycle: data exploration to deployment to monitoring
- Causal inference, statistics, and optimization fundamentals
- Backtesting and validation framework design
- Translating ambiguous business problems into scoped modeling work
- Communicating technical results to product/risk/business stakeholders
Common question themes
Design a credit risk scoring model from scratch — what data, features, and validation approach
How would you evaluate a new (possibly unstructured) data source for inclusion in a credit model
Describe a time you shipped an ML model to production and it degraded — how did you detect and fix it
Explain a causal inference technique you've used to isolate a real business effect
How do you balance model complexity against interpretability for a risk decision that affects customers
Tell me about translating an ambiguous ask from a business partner into a modeling roadmap
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