Data Scientist, Finance

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 decisions that shape the outcome.

The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same.

If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it.

What You’ll Do

Full stack development, building models to consume, transform, and expose data to stakeholders and production systems

Drive a culture of experimental design, testing agenda, and best practices

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

Collaborate with Finance teams (e.g. GTM Finance, StratFin) to develop financial insights and influence business decisions

Work closely with data engineering teams to capture, move, store, and transform raw data into highly actionable insights, and partner with business teams to turn those insights into action

What You Need

Minimum of 3 years of industry experience in Data Science / Software Engineering / Finance

Strong AI proficiency as a lever to quickly adopt new skills and subject matter

Track record of shipping high quality products and features at scale

Ability to thrive in a fast-paced, constantly improving, start-up environment that focuses on solving problems with iterative technical solutions

Familiarity with financial metrics and processes (e.g. contribution profit, financial statements, monthly close) and / or B2B enterprise sales cycle metrics and processes

Strong knowledge of SQL (preferably Redshift, Snowflake, BigQuery) and how to write efficient SQL queries

Nice-to-Haves

Experience with the modern data stack (Fivetran / Snowflake / dbt / Looker / Hightouch or equivalents)

Familiarity with BI tools (preferably Looker, Omni, Sigma, Hex or equivalent) and experience distributing data insights via reports and dashboards

Strong intuition on business strategy and customer empathy (ROI, growth channels, sales cycles, customer incentives)

Strong perspective on analytics engineering development cycle (data modeling, version control, documentation + testing, best practices for codebase development)