What You’ll Do
- Apply statistical, machine learning, and econometric techniques to large, complex datasets to evaluate channel performance and measure the causal impact of marketing and sales initiatives across a nuanced enterprise sales cycle.
- Develop attribution models and investment frameworks to guide Ramp’s brand and channel strategy, helping finance and marketing teams scale efficiently and understand which messages resonate with each audience throughout the customer journey.
- Partner closely with Martech, Business Systems, and Growth Engineering teams to integrate and enrich first- and third-party data, ensuring decisions are made with maximum context and accuracy.
- Design and execute experiments across new channels and surfaces, enabling rapid, cost-effective iteration—particularly for marketing efforts focused on awareness, consideration, and brand equity.
- Contribute to Ramp’s data culture by improving processes, tools, and systems that enable scalable, high-quality decision-making across the organization.
What You Need
- Bachelor’s degree or higher in Math, Economics, Statistics, Engineering, Computer Science, or a related quantitative field, with 5+ years of industry experience as a Data Scientist.
- Strong proficiency in Python (e.g., NumPy, pandas, scikit-learn) for exploratory analysis, predictive modeling, and applying machine learning to marketing use cases.
- Advanced SQL skills, preferably with Snowflake, BigQuery, or Redshift.
- Demonstrated leadership and a track record of delivering impactful improvements in collaboration with growth and product teams.
- Deep understanding of the marketing experimentation lifecycle, including hypothesis generation, experimental design, implementation, and A/B testing best practices.
- Strong point of view on marketing attribution, martech ecosystems, and the evolving privacy landscape.
- Ability to thrive in a fast-paced, iterative startup environment focused on solving complex problems with practical technical solutions.
Nice to Have
- Experience at a high-growth startup.
- Familiarity with B2B enterprise sales cycles, metrics, and processes.
- Hands-on experience with the modern data stack (e.g., Fivetran, Snowflake, dbt, Looker, Hex, Hightouch, or equivalents).
- Experience with data orchestration tools such as Airflow, Dagster, or Prefect.
- Strong understanding of the data science engineering lifecycle, including data modeling, version control, documentation, testing, and codebase best practices.