Senior Data Scientist | Growth

ABOUT RAMP At 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 https://www.fastcompany.com/91038883/ramp-most-innovative-companies-2024 list and LinkedIn’s Top U.S. Startups https://www.linkedin.com/pulse/linkedin-top-startups-2024-50-us-companies-rise-linkedin-news-hxote/?trackingId=uBI29YlAOxikbTI7cdvG4g%3D%3D for more than 3 years, as well as the Forbes Cloud 100 https://www.forbes.com/sites/richardnieva/2024/08/06/ramp-cloud-100/, CNBC Disruptor 50 https://www.cnbc.com/2024/05/14/ramp-cnbc-disruptor-50.html, and TIME Magazine’s 100 Most Influential Companies https://time.com/collection/time100-companies-2023/6285147/ramp/.

ABOUT THE ROLE We’re looking for someone to help lead the future of growth at Ramp. In this role, you will help define the analytical frameworks and strategic roadmaps for how Ramp’s growth teams optimize and scale our marketing investments across all brand channels. You will partner closely with marketing, finance, and engineering counterparts across experimental design, statistical modeling, implementation, execution, and analysis.

Our goal is to efficiently reach the right user with the right message at the right time. Ultimately, we will depend on you to co-own the allocation of millions of dollars per month in brand marketing spend. WHAT YOU’LL DO - Employ statistical, machine learning, and econometric models on large datasets to evaluate channel performance and discern the causal impact of marketing and sales campaigns on a complex and nebulous enterprise sales cycle - Build attribution models and investment frameworks to inform Ramp’s future brand channel investments, allowing Ramp’s finance and marketing teams to scale efficiently and understand which message resonates with each audience segment at each point in the customer journey - Partner closely with Martech, Business Systems, and Growth Engineering 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 - Drive experimental design and implementation on new channels and surface areas of Ramp, ensuring we can iterate quickly and cost-effectively, especially on marketing spend designed to build awareness, consideration, and brand equity - Contribute to the culture of Ramp’s data 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 Data Scientist - Strong python experience (numpy, pandas, sklearn, etc.) across exploratory data analysis, predictive modeling, and applications of ML techniques to marketing-specific problems - Strong knowledge of SQL (preferably Snowflake, BigQuery, or Redshift) - Proven leadership and a track record of shipping improvements with growth and product organizations - Strong perspective on the marketing experimentation lifecycle (hypothesis generation, experimental design, implementation, statistical analysis, A/B testing best practices) - Deep familiarity with the past, present, and future of marketing attribution, martech, and the modern privacy landscape - 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 - Familiarity with B2B enterprise sales cycle metrics and processes - Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Hex / Hightouch 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) 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 $10,000 per year) - Parental Leave - Pet insurance - Centralized home-office equipment ordering for all employees - Health and Wellness stipend - In-office perks: lunch, snacks, drinks, and more - Budget for intra-office travel - Relocation support to NYC or SF (as needed) REFERRAL INSTRUCTIONS If you are being referred for the role, please contact that person to apply on your behalf.

OTHER NOTICES Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Ramp Applicant Privacy Notice https://ramp.com/legal/applicant-privacy-notice