Analyze customer, marketing, and onsite data to improve campaigns, build and evaluate machine-learning models, automate insight delivery, and develop marketing-focused self-service BI solutions on GCP.
Data Scientist, Finance
📜 Description
- Build models and data workflows that consume, transform, and expose data to stakeholders and production systems.
- Promote experimental design, testing, and data best practices.
- Shape the data team's processes, tools, and systems to support scalable decision-making.
- Partner with Finance teams to develop financial insights and influence business decisions.
- Work with data engineering and business teams to turn raw data into actionable insights and action.
🛠️ Requirements
- Minimum of 3 years of industry experience in Data Science, Software Engineering, or Finance.
- Strong AI proficiency and ability to quickly adopt new skills and subject matter.
- Track record of shipping high-quality products and features at scale.
- Familiarity with financial metrics and processes or B2B enterprise sales cycle metrics and processes.
- Strong SQL knowledge, including writing efficient queries; experience with Redshift, Snowflake, or BigQuery preferred.
- Experience with modern data stack tools, BI tools, analytics engineering practices, or finance teams is a plus.
✨ Benefits
- Flexible PTO
- Centralized home-office equipment ordering
- Health and wellness stipend
- Budget for intra-office travel
- Weekly coffee stipend
Full job description
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 $275B in annualized spend flows in and out of 85,000+ 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 7.5% 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)
Experience partnering with finance teams or within the payments and financial technology space
Benefits available to all full-time Ramp employees (Global)
Flexible PTO
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 expense coverage to NYC or SF (if 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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