About Ramp
Ramp is the ultimate platform for modern finance teams. Combining corporate cards with expense management, bill payments, vendor management, accounting automation and more, Ramp's all-in-one solution is designed to save businesses time and money, and free finance teams to do the best work of their lives. Our mission is to help build healthier businesses, and it’s working: over 15,000 businesses on Ramp to save an average 5% and close their books 8x faster.
Founded in 2019, Ramp powers the fastest-growing corporate card and bill payment platform in America, and enables tens of billions of dollars in purchases each year.
Ramp's investors include Founders Fund, Stripe, Citi, Goldman Sachs, Coatue Management, D1 Capital Partners, Redpoint Ventures, General Catalyst, and Thrive Capital, 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. In 2023, Ramp was named Fast Company’s #1 Most Innovative Company in North America, LinkedIn’s #1 Top Startup in the U.S., a CNBC Disruptor, and a TIME100 Most Influential Company.
About the Role
We’re looking for someone to lead the future of fraud applied science at Ramp. In this role, you will help build core machine learning, design data architectures, as well as, set strategic roadmaps to help Ramp reduce fraud on the platform. You will partner closely with risk and engineering counterparts across model design, implementation, execution, and analysis. Our goal is to provide a frictionless experience for every legitimate Ramp user.
What You’ll Do
Employ statistical, machine learning, and econometric models on large datasets to discover patterns of fraud
Prototype and productionalize machine learning models and rules-based systems to prevent fraud
Partner closely with Risk Engineering and Data Platform teams to augment and leverage data across first and third party sources, ensuring we’ve added as much context as possible to every decision we make
Contribute to the culture of Ramp’s applied science team by influencing processes, tools, and systems that will allow us to make better decisions in a scalable way
What You Need
Bachelor’s degree or above in Math, Economics, Bioinformatics, Statistics, Engineering, Computer Science, or other quantitative fields with a minimum of 5 years of industry experience as a Machine Learning Engineer, Applied Scientist or Data Scientist
Strong python experience (numpy, pandas, sklearn, pytorch etc.) across exploratory data analysis, predictive modeling, and applications of ML techniques
Prior experience deploying Machine Learning models to production
Strong knowledge of SQL (preferably Redshift, Snowflake, BigQuery)
Proven leadership and a track record of shipping improvements with growth and product organizations
Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions
Nice-to-Haves
Experience at a high-growth startup
Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Census or equivalents)
Familiarity with data orchestration platforms (Airflow, Dagster, Prefect)
Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)
Compensation
The annual salary/OTE range for the target level for this role is $191,250 - $225,000 + target equity + benefits (including medical, dental, vision, and 401(k)
Benefits (for U.S.-based full-time employees)
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
Pet insurance