Senior Data Scientist, Growth

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.

About the Role

We're looking for someone to help lead the future of growth at Ramp. In this role, you will help define the analytical frameworks and strategic roadmaps for how Ramp's growth teams optimize and scale our marketing investments across all brand channels. You will partner closely with marketing, finance, and engineering counterparts across experimental design, statistical modeling, implementation, execution, and analysis. Our goal is to efficiently reach the right user with the right message at the right time. Ultimately, we will depend on you to co-own the allocation of millions of dollars per month in brand marketing spend.

What You'll Do

• Employ 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

• Build 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

• Partner 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

• Drive 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

• 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

What You Need

• 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

Nice-to-Haves

• Experience at a high-growth startup

• Familiarity with B2B enterprise sales cycle metrics and processes

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

• Familiarity with data orchestration platforms (Airflow, Dagster, Prefect)

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

Benefits Available to All Full-Time Ramp Employees (Global)

• Flexible PTO

• Unlimited AI token usage

• Centralized home-office equipment ordering

• Health and wellness stipend

• Budget for intra-office travel

• Weekly coffee stipend

United States

• 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents

• One Medical annual membership

• 401(k), including employer match on contributions made while employed by Ramp

• Fertility HRA (up to $10,000 per year)

• Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay

• Pet insurance

• In-office perks: lunch, snacks, drinks, and more

• Relocation support to NYC or SF (as needed)

Canada

• Group medical, dental, and vision coverage through Sun Life

• Life, AD&D, and disability coverage

• Fertility drug coverage (up to $4,000 lifetime)

• Group Retirement Plan with employer match (RRSP + DPSP)

• Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay, with additional time available at reduced pay

• Employee Assistance Program and virtual care through Lumino Health

United Kingdom

• Private medical insurance through Freedom Elite

• Virtual GP and at-home care via eMed x Livi

• Workplace pension through Penfold, with salary sacrifice option

• Parental leave: up to 16 weeks (birthing + bonding) or 8 weeks (bonding only) at 100% pay with additional time available at reduced pay

Referral Instructions

If you are being referred for the role, please contact that person to apply on your behalf.

Other Notices

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Beware of recruiting scams: Ramp will only contact you through official @ Ramp.com email addresses and will never ask for payment or sensitive personal information during the hiring process.

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