Credit Risk Associate

ABOUT RAMP

Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books.

The problems are high-stakes, data-dense, and unforgiving.

We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome.

The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.

If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.

As a member of Ramp's Risk Strategy & Operations team, you will leverage data to develop and optimize credit strategies.

Credit is one of Ramp's most consequential products. Every policy change affects how customers can grow their usage, and how responsibly Ramp scales up while managing risk. Credit Risk Strategy owns these tradeoffs end to end.

In this role, you will own or help build strategy across credit risk areas like credit limits, payment speed, and collections. You'll take ambiguous problems, get to the data, prototype the solution, and push the change with Product, Engineering, Data Science, Risk Operations, Finance, Customer Experience, and Compliance.

AI fluency is required. We use AI as a core pillar of our risk management stack, and you will be prototyping and managing related Agents and tools. You should already be using tools like Claude Code, Codex, or similar AI tools to write code, explore data, prototype apps, automate workflows, and check your own work. You do not need to be a software engineer, but you do need to use AI to ship something runnable or inspectable enough to test, improve, and hand off.

We are looking for builders with high agency and high urgency: people who prototype the workflow, not just write the recommendation for someone else to build.

What You'll Do

• Own credit risk strategy for areas like model prototyping, credit limits, payment speed, collections, etc.

• Use SQL, quantitative reasoning, and credit risk judgment to investigate patterns, size opportunities, define policy changes, and pressure-test recommendations.

• Build the first useful version when the workflow does not exist: a tool, app, dashboard, agent, notebook, QA loop, monitor, or decisioning process.

• Use AI tools every day to move faster on research, analysis, coding, synthesis, writing, verification, and follow-through.

• Build AI into credit risk workflows: feature exploration, policy monitoring, case review, exception handling, decision support, documentation, and human-in-the-loop QA.

• Investigate new data sources and model features; evaluate signal quality, coverage, failure modes, and how they would change credit decisions.

• Make ambiguous credit risk decisions within your surface area, balancing loss, customer experience, operational burden, growth, compliance, and risk-adjusted returns.

• Partner with Product, Engineering, Design, to execute and build the risk management infrastructure

What You Need

• Minimum 2 years of experience in credit risk management or quantitative strategy role

• Minimum 2 years of experience using SQL or Python for data retrieval and manipulations

• AI fluency you can demonstrate live: name the tools, show an artifact, explain a recent failure mode, and walk through how you verified the output before using it in a credit risk decision.

• Ownership in ambiguity: you define the question, get the data, make the call, communicate the tradeoffs, and drive follow-through without waiting for perfectly scoped work.

• Strong communication:

Responsibilities

• Own credit risk strategy for areas like model prototyping, credit limits, payment speed, collections, etc.
• Use SQL, quantitative reasoning, and credit risk judgment to investigate patterns, size opportunities, define policy changes, and pressure-test recommendations.
• Build the first useful version when the workflow does not exist: a tool, app, dashboard, agent, notebook, QA loop, monitor, or decisioning process.
• Use AI tools every day to move faster on research, analysis, coding, synthesis, writing, verification, and follow-through.
• Build AI into credit risk workflows: feature exploration, policy monitoring, case review, exception handling, decision support, documentation, and human-in-the-loop QA.
• Investigate new data sources and model features; evaluate signal quality, coverage, failure modes, and how they would change credit decisions.
• Make ambiguous credit risk decisions within your surface area, balancing loss, customer experience, operational burden, growth, compliance, and risk-adjusted returns.
• Partner with Product, Engineering, Design, to execute and build the risk management infrastructure

Requirements

• Minimum 2 years of experience in credit risk management or quantitative strategy role
• Minimum 2 years of experience using SQL or Python for data retrieval and manipulations
• AI fluency you can demonstrate live: name the tools, show an artifact, explain a recent failure mode, and walk through how you verified the output before using it in a credit risk decision.
• Ownership in ambiguity: you define the question, get the data, make the call, communicate the tradeoffs, and drive follow-through without waiting for perfectly scoped work.
• Strong communication