Summary

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

1. 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
2. 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
3. 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
4. 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
5. 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

1. 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
2. Strong python experience (numpy, pandas, sklearn, etc.) across exploratory data analysis, predictive modeling, and applications of ML techniques to marketing-specific problems
3. Strong knowledge of SQL (preferably Snowflake, BigQuery, or Redshift)
4. Proven leadership and a track record of shipping improvements with growth and product organizations
5. Strong perspective on the marketing experimentation lifecycle (hypothesis generation, experimental design, implementation, statistical analysis, A/B testing best practices)
6. Deep familiarity with the past, present, and future of marketing attribution, martech, and the modern privacy landscape
7. Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions

Nice-to-Haves

1. Experience at a high-growth startup
2. Familiarity with B2B enterprise sales cycle metrics and processes
3. Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Hex / Hightouch or equivalents)
4. Familiarity with data orchestration platforms (Airflow, Dagster, Prefect)
5. Strong perspective on data science engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)

Skills

• Database Management
• Growth Strategy
• Machine Learning
• Software Engineering
• SQL