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 40,000 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 $80 billion in purchases each year.
Ramp’s investors include Thrive Capital, Sands Capital, General Catalyst, Founders Fund, Khosla Ventures, Sequoia Capital, Greylock, and Redpoint, 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.
We’re looking for someone to help lead the future of credit applied science at Ramp. The Applied Science team at Ramp creates value by building the models powering decision-making. You will need to have a head for strategy & cross-functional collaboration, since you will partner closely with business & product stakeholders to prioritize, execute, and drive results through improving our Credit Risk decisioning systems. You will also partner closely with the rest of the data team and the engineering team to design, implement, and maintain data science models in production.
Applied scientists at Ramp focuses on solving quantitative problems across credit, fraud, growth, and our core product by applying the right mix of causal inference, structural modeling, and optimization.
What You’ll DoFull stack data science development: from upstream data modeling and cleaning, to research and prototyping, to deploying and monitoring models in production and evaluating their business impact
Contribute to the company roadmap by working closely with stakeholders throughout the lifecycle of prioritization: from complex and nebulous business context, to well-defined objectives, to a roadmap of scoped opportunities for leveraging data science to drive business results
Improve model performance through new and improved data sources (e.g., accounting and bank statement parsing), advanced feature engineering, and model architecture enhancements
Ship production-grade ML pipelines including backtesting, retraining, drift monitoring, and business metric attribution
Design model evaluation and reporting frameworks that satisfy both internal stakeholders and external banking partners
Contribute to strategic decisions around risk policy and product expansion through ML-backed insights
Collaborate cross-functionally with engineering, product, and risk strategy teams to integrate models and optimize customer-level outcomes
Bachelor’s degree or above in Math, Economics, Physics, Computer Science, or other quantitative fields.
For candidates with Bachelors and Master’s, minimum of 5 years of industry experience as a Data Scientist, Applied Scientist, or equivalent
Experience working with large datasets in Python and SQL
Strong familiarity with the mathematical fundamentals of advanced statistics, optimization, and/or economics, as well as methods for experimental design and causal inference
Experience developing, deploying and maintaining ML systems, including understanding of model performance monitoring, retraining, and feedback loop management in live environments
Strong communication: the ability to bridge technical methodology to meaningful data narratives to drive company decisions and strategy
Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on delivering customer and business impact with iterative technical solutions
Ability to break down complex problems rigorously and understand the tradeoffs necessary to deliver impactful roadmaps and projects
PhD in Math, Economics, Physics, Computer Science, or other quantitative fields
Experience shipping or maintaining credit risk models, fraud models, or regulated ML systems is a strong plus
Experience collaborating with cross-functional teams to deploy models that directly impact revenue or loss metrics
Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)
Experience at a high-growth startup
100% medical, dental & vision insurance coverage for you
Partially covered for your dependents
One Medical annual membership
401k (including employer match on contributions made while employed by Ramp)
Flexible PTO
Fertility HRA (up to $5,000 per year)
WFH stipend to support your home office needs
Wellness stipend
Parental Leave
Relocation support to NYC or SF
Pet insurance
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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