RAMP

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.. 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 knowledge of SQL (preferably Redshift, Snowflake, BigQuery). Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Census or equivalents)

applied scientist

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 growth data science 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 channels. You will partner closely with marketing, finance, and engineering counterparts across experimental design, implementation, execution, and analysis. Our goal is to reach the right user with the right message at the right time. Ultimately, we will depend on you to develop best practices across all of Ramp for marketing experimentation and data-driven investment decisions. What You'll Do <ul><li>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 </li><li>Build attribution models and investment frameworks to inform Ramp's future channel investments, allowing Ramp's finance and marketing teams to scale efficiently </li><li>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 </li><li>Experimental design and implementation on new channels and surfaces areas of Ramp, ensuring we can iterate quickly and cost-effectively, especially on marketing spend designed to build awareness, consideration, and brand equity </li><li>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 </li></ul>What You Need<ul><li>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 </li><li>Strong python experience (numpy, pandas, sklearn, etc.) across exploratory data analysis, predictive modeling, and applications of ML techniques to marketing-specific problems </li><li>Strong knowledge of SQL (preferably Redshift, Snowflake, BigQuery) </li><li>Proven leadership and a track record of shipping improvements with growth and product organizations </li><li>Strong perspective on the marketing experimentation lifecycle (hypothesis generation, experimental design, implementation, statistical analysis, A/B testing best practices) </li><li>Deep familiarity with the past, present, and future of marketing attribution, martech, and the modern privacy landscape, especially as it pertains to B2B SaaS GTM motions </li><li>Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions </li></ul>Nice-to-Haves<ul><li>Experience at a high-growth startup </li><li>Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Census or equivalents) </li><li>Familiarity with data orchestration platforms (Airflow, Dagster, Prefect) </li><li>Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development) </li></ul>Compensation<ul><li>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) </li></ul>Benefits (for U.S.-based full-time employees) <ul> <li>100% medical, dental & vision insurance coverage for you <ul> <li>Partially covered for your dependents </li><li>One Medical annual membership </li></ul> </li><li>401k (including employer match on contributions made while employed by Ramp) </li><li>Flexible PTO </li><li>Fertility HRA (up to $5,000 per year) </li><li>WFH stipend to support your home office needs </li><li>Wellness stipend </li><li>Parental Leave </li><li>Relocation support </li><li>Pet insurance </li> </ul>

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