Senior Data Scientist, Growth

The role

This senior data scientist role focuses on establishing analytical frameworks and strategic direction for Ramp's growth initiatives. You'll partner with marketing, finance, and engineering teams to optimize brand channel investments and understand marketing impact on enterprise sales cycles. The position owns millions in monthly marketing spend allocation decisions.

What you would do

• Develop statistical, machine learning, and econometric models to evaluate channel performance and campaign impact on complex enterprise sales processes
• Build attribution models and investment frameworks guiding brand channel allocation and audience segmentation across customer journey stages
• Collaborate with marketing technology, business systems, and engineering teams to integrate first and third-party data sources
• Design and implement experiments for new channels and marketing initiatives, prioritizing rapid iteration and cost efficiency
• Strengthen data team culture by improving processes, tools, and decision-making systems at scale

What they asked for

• Bachelor's degree or higher in quantitative field such as mathematics, economics, statistics, engineering, or computer science
• Minimum five years as a data scientist in industry roles
• Proficiency in Python including libraries for data analysis and machine learning
• Advanced SQL skills, ideally with cloud data warehouse platforms
• Demonstrated leadership shipping improvements with growth and product teams
• Deep expertise in marketing experimentation, hypothesis testing, and A/B testing methodology
• Comprehensive knowledge of marketing attribution, martech ecosystem, and privacy considerations
• Ability to operate effectively in fast-paced startup environments with iterative problem-solving

Nice to have

• Background at high-growth startups
• Familiarity with B2B enterprise sales metrics and workflows
• Experience with modern data stack components including Fivetran, dbt, Looker, Hex, or Hightouch
• Knowledge of data orchestration platforms such as Airflow, Dagster, or Prefect
• Strong data science engineering practices including modeling, version control, documentation, and testing