USD per year
- About
Who you are You’ve operated in billing + accounts receivable environments and you understand how cash actually moves You’re calm with messy data and ambiguous inputs (CSV exports, inconsistent fields, partial histories) You communicate clearly with finance stakeholders and can explain discrepancies without drama You’re systems-minded: you naturally think in workflows, edge cases, owners, and “what breaks if we change this?” You’re a leverage-seeker: you turn recurring migration steps into templates, tooling, and automation What the job involves Help customers migrate from their current billing/AR setup into Alguna without breaking revenue workflows Translate real-world AR processes (terms, invoicing, collections, disputes, cash application) into a clean system configuration Own “cutover readiness”: open invoices, credits, unapplied cash, and reconciliation checks before go-live Validate outcomes: invoice parity, AR aging integrity, payment matching, and customer-facing billing accuracy Partner with customer finance teams to resolve exceptions fast (pricing mismatches, missing payments, credit application issues) Create repeatable playbooks/checklists so each migration gets faster and more reliable Requirements (past experience) 2–6 years in Billing Ops / AR / Collections / Finance Ops Hands-on experience with: AR aging and collections workflows Dispute management Reconciliation (invoices ↔ payments ↔ credits) Cash application / unapplied cash (or close equivalent) Strong spreadsheet skills (Excel/Google Sheets); comfortable doing reconciliation logic Experience working with finance systems (any of: Stripe, Netsuite, Xero, Sage, QuickBooks, Chargebee, Zuora, Salesforce/HubSpot) Nice to have SaaS subscription or usage-based billing exposure Experience supporting implementations/migrations (even internally) Comfort writing lightweight automation (Sheets scripts, Zapier, SQL) or using AI tools to accelerate ops work. Visa requirement: US citizenship/visa not required.
Liva AI is focused on making voice AI feel human by creating datasets from real conversations that capture emotion, nuance, context, and intent to train speech and multimodal models.
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