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Card / Consumer Lending FP&A Analyst (Contract, Remote)

Patronus AI Patronus AI ContractRemote — United States $55–125/hr
Posted Oct 28, 2026 · ✓ Still open — checked 1 hour ago

Quick facts

Pay
$55–$125/hr
Type
Contract
Location
Remote — United States
Hours
10–20 hours / week
Platform
Patronus AI — 26 open jobs
Typical pay at Patronus AI
$45–150/hour · median $90 (21 jobs on our site)
Payout
Contractor payments (method not published) (reported by workers)
Apply
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Pay: $55–$125/hr

Commitment: Flexible, typically 10–20 hours per week

Patronus AI is a frontier lab building simulation research and infrastructure to accelerate progress toward human-aligned AGI. We built some of the earliest research in AI evaluation, including FinanceBench, Lynx, SimpleSafetyTests, CopyrightCatcher and Humanity's Last Exam. Our team comes from Meta AI, Amazon AGI and Google. Our customers include foundation model labs and Fortune 500 companies, and we are backed by Lightspeed Venture Partners, Notable Capital and others.

We're looking for FP&A analysts in card or consumer lending who do this work today. In your role you build budgets, forecasts and variance analysis for a card or consumer lending business, and own the driver-based models and management packs behind them. You'll help us show frontier AI models how that work is really done, and where they get it wrong. You don't need an AI background.

What you'll do

  • Explain how the work is done in your role: the steps, the documents, who decides what, and where things go wrong.
  • Review realistic task packets, such as driver-based forecast models, budget and variance reports, management and board packs, and loss, NIM and interchange analyses, and flag anything a practitioner would find unrealistic.
  • Write scoring rubrics precise enough that two independent reviewers reach the same verdict.
  • Create new, original tasks from fictional scenarios, solve them yourself and document your reasoning.
  • Work independently and asynchronously, meet agreed deadlines and work in reviewer feedback.