Fraud Strategy Analyst (All Levels)
RampRemote$102k – $220kPosted 23 February 2026
Job Description
About RampAt Ramp, we’re rethinking how modern finance teams function in the age of AI. We believe AI isn’t just the next big wave. It’s the new foundation for how business gets done. We’re investing in that future — and in the people bold enough to build it.Ramp is a financial operations platform designed to save companies time and money. Our all-in-one solution combines payments, corporate cards, vendor management, procurement, travel booking, and automated bookkeeping with built-in intelligence to maximize the impact of every dollar and hour spent. More than 50,000 businesses, from family-owned farms to e-commerce giants to space startups, have saved $10B and 27.5M hours with Ramp. Founded in 2019, Ramp powers the fastest-growing corporate card and bill payment platform in America, and enables over $100 billion in purchases each year.Ramp’s investors include Lightspeed Venture Partners, Thrive Capital, Sands Capital, General Catalyst, Founders Fund, Khosla Ventures, Sequoia Capital, Greylock, Redpoint, and ICONIQ, as well as over 100 angel investors who were founders or executives of leading companies. The Ramp team comprises talented leaders from leading financial services and fintech companies—Stripe, Affirm, Goldman Sachs, American Express, Mastercard, Visa, Capital One—as well as technology companies such as Meta, Uber, Netflix, Twitter, Dropbox, and Instacart.Ramp has been named to Fast Company’s Most Innovative Companies list and LinkedIn’s Top U.S. Startups for more than 3 years, as well as the Forbes Cloud 100, CNBC Disruptor 50, and TIME Magazine’s 100 Most Influential Companies.About the RoleAs a member of Ramp's Fraud Strategy team, you will leverage data to develop and optimize fraud strategies, including underwriting (KYC/B, OFAC, fraud risk) and customer management (payment, transactions, account take-over) strategies. This role requires an understanding of fraud concepts & data sources, fluency with data, and the ability to drive a broad set of projects fairly independently. The role also requires the ability to work with cross-functional teams (product, engineering, operations, finance, marketing, sales, and design) to influence and execute risk strategies. You will be given significant ownership of your projects, with the freedom and autonomy to innovate and execute your ideas, empowering you to do your best work and make a meaningful impact. As you successfully drive impactful changes to our fraud prevention policies, you will be entrusted with larger projects and more strategic initiatives, expanding your influence within the company.What You’ll DoLeverage external and internal performance data to come up with insights to develop best-in-class fraud policies (e.g. identity/first-party fraud, transaction fraud, account takeover fraud, KYC/B, etc.) that will create value for Ramp and its customersDeep dive into individual fraud events to perform root cause analyses, and combine the insights with a data-driven approach to develop fraud defensesPartner with product, engineering, and design teams to execute fraud policies and featuresImplement fraud risk metric monitoring by researching/resolving risk events, and developing levers that mitigate risk factorsPartner with data scientists to build and leverage fraud risk models in Ramp’s risk policyDrive Ramp’s customer experience by diving deep into the flow of funds (repayments, chargebacks, etc.) Research emerging underwriting and fraud trends to innovate on Ramp's decision systems and policiesWhat You NeedMinimum 2 years of experience in fraud risk management or quantitative strategy roleMinimum 2 years of experience using SQL, Python, or R for data retrieval and manipulation Excellent written and verbal communication skillsNice to HavesExperience in high-growth startupsExperience within consumer/corporate/small business cards, payments, invoices, lending, or related industriesWe are hiring analysts across multiple levels. If you are excited to work on complex ... (truncated, view full listing at source)
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