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About This RoleAI processing…
About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential
Key Responsibilities
- 1Employ 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
- 2Build 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
- 3Partner 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
- 4Drive 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
- 5Contribute 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
Requirements
- 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
Perks & Benefits
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