AI-Native Finance Analyst

Location: Trade Tower, Thapathali, Kathmandu, Nepal

Type: Full-time (On-site, evening/night shift aligned to US Eastern Time)

Compensation: NPR 1,50,000 – 2,00,000 per month

THE SHORT VERSION

Work for a California-based company. Get trained — properly, on AI workflows, with mentorship — to run AI-automated finance operations that most finance professionals are still only reading about. No prior AI experience required. Real AI experience guaranteed. All you need is enthusiasm and a problem-solving mindset.

ABOUT COVU

COVU is building the AI-native operating stack for insurance distribution — combining software, services, markets, and capital. We partner with, operate, and acquire independent insurance agencies to turn fragmented service businesses into a scalable platform with data, automation, and product at the core.

COVU is backed by over $32 million in funding from Silicon Valley investors — a well-capitalized, growing US company. Our Kathmandu office is an early and expanding part of that growth: join now and you’re building the team, not joining the back of one.

WHY THIS ROLE IS DIFFERENT

Most finance jobs teach you yesterday’s workflow: manual entries, manual reconciliations, manual reports. This role is the opposite. Here, AI agents draft the journal entries, prepare the reconciliations, and assemble the reports — and YOU are the human-in-the-loop the machine answers to. You review, approve, correct, and improve AI-prepared work before it touches the books of a multi-entity US operation. Every correction you make becomes training signal. Over time you automate parts of your own job — and graduate to supervising more workflows. This is the career path forward-looking finance professionals are competing for. We train you for it.

WHAT YOU’LL DO

• Review AI-prepared journal entries, payables packets, receivables follow-ups, and reconciliation work in an approval queue — approve, correct, or escalate
• Work exception queues in our ticketing and messaging tools: investigate the unusual, resolve it, document the pattern
• Quality-check automation output against source data (book vs bank statements) — your sign-off is the control
• Write playbook specifications: the documents that define how each workflow runs and where humans step in
• Use AI tools (LLM-based agents, prompts, structured workflows) hands-on

THE TRAINING — STRUCTURED, NOT “FIGURE IT OUT”

• Weeks 1–2: US finance operations bootcamp — multi-entity accounting, month-end close, payables/receivables, audit-ready documentation
• Weeks 3–12: AI workflow training — how to operate, test, and correct LLM-based agents and workflows; prompt and playbook writing; human-in-the-loop discipline
• Ongoing: direct, hands-on mentorship from the person who built the automation system — not a training video. As your skills grow, your responsibilities grow with them: there is no fixed ceiling on this role

WHO YOU ARE

• 2–4 years in finance operations, accounting, data operations, or analytical back-office / BPO or QA work
• Excellent written and spoken English — clear written updates, clean tickets, precise documentation
• Detail-obsessed: you catch the one-rupee/dollar mismatch and want to know why it happened
• Fast with spreadsheets and new AI tools; excited by AI, not threatened by it
• Works independently without real-time oversight — comfortable owning a queue while US colleagues are offline
• Comfortable with a fixed evening/night schedule aligned to US Eastern Time (ET)
• Bonus: experience with any accounting or bookkeeping system, SQL basics, data-annotation/QA experience, BBA/BBS/ACCA in progress

SHIFT LOGISTICS & BENEFITS

• Working Hours: 9:00 AM – 6:00 PM US Eastern Time (ET).
  • In Kathmandu, that translates to:
    • 6:45 PM – 3:45 AM NPT during US Daylight Saving Time (roughly March–November).
    • 7:45 PM – 4:45 AM NPT during US Wintertime.
  • Shift Nature: This is a permanent night-shift role.
  • Time Off: Leaves provision.

HIRING PROCESS — UNDER 3 WEEKS END TO END

1. Application + short written screen (a realistic exception-handling scenario, ~30 minutes)
2. Practical assessment — spot the errors in an AI-prepared finance packet
3. Working session with the hiring manager and Kathmandu team lead
4. Offer