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Ramp is building intelligent infrastructure for finance teams, automating how over $200B in annualized spend flows through 70,000+ companies. The company handles payment authorization, risk flagging, spend categorization, and financial close processes at scale.
In this Data Scientist role, you'll own full-stack data development—building models to consume, transform, and expose data to both stakeholders and production systems. You'll drive a culture of experimental design and testing best practices while contributing to Ramp's data team infrastructure, tools, and decision-making processes.
Key responsibilities include collaborating with Finance teams (GTM Finance, StratFin) to develop financial insights that influence business decisions, and partnering closely with data engineering to capture, move, store, and transform raw data into actionable insights. You'll work across the organization to turn insights into action.
You'll need a minimum of 3 years in Data Science, Software Engineering, or Finance, with strong SQL proficiency (Redshift, Snowflake, or BigQuery). AI proficiency is essential as a lever for quickly adopting new skills and domain knowledge. You should have a track record of shipping high-quality products at scale and thrive in fast-paced startup environments.
Familiarity with financial metrics (contribution profit, financial statements, monthly close) and/or B2B enterprise sales cycle metrics is required. Nice-to-haves include experience with the modern data stack (Fivetran, Snowflake, dbt, Looker, Hightouch), BI tools (Looker, Omni, Sigma, Hex), business strategy intuition, and prior experience in fintech or payments.
