Ramp

Ramp

Product Operations Specialist

New York, NY (HQ) · Full-time

Sponsorship not specifiedDetected 9 days ago
PythonJavaReactSQLData AnalysisUX ResearchAccountingProcurementResearchCommunicationAccounts PayableUnderwriting

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odds of building a lasting career here

40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70

Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.

Lottery odds assume a STEM candidate.

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About the role

  • Please note that this Product Operations role will require you to be comfortable with working in-person at our NYC HQ (located near Madison Square Park) at least 2 days/week*

Responsibilities

  • Experience leveraging qualitative and quantitative feedback to create product recommendations
  • Comfort in ambiguity and a track record of charting your own path

Requirements

  • Experience with Retool or other low-code tooling platforms

Compensation

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

Benefits

  • Centralized home-office equipment ordering
  • Health and wellness stipend
  • Weekly coffee stipend
  • 100% medical, dental & vision insurance coverage for you, with partial coverage for dependents
  • One Medical annual membership
  • Relocation expense coverage to NYC or SF (if needed)
  • 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

Company info

  • Experience working directly with customers or clients through feedback requests and/or user research interviews

This listing is sourced directly from Ramp's careers page and normalized into a canonical job model.