AI Data Scientist

We’re building the agentic financial ops API for SMB platforms. Fintechs, vertical SaaS, banks, and PSPs use Dolfin to embed AI-native financial ops agents (invoicing, bills, expenses etc) directly into their products. White-label, API-first, production-ready., * Own extraction and matching across AR, AP, expenses and more. Invoices, purchase orders, remittances, bill, receipts, card transactions. Unstructured PDFs, photos, and structured e-invoicing feeds.

• Own confidence scoring and threshold calibration. Auto-accept vs elevate is the highest-leverage decision in the product. Too loose and we post bad entries to a real ledger. Too tight and the platform’s customers do the work themselves.
• Solve cold start. New platforms go live in days with almost no labelled data for their formats, chart of accounts, or policies. Making that work without a bespoke build per client is an open problem, and it’s yours.
• Build the evaluation infrastructure. Gold sets, regression suites, per-platform accuracy tracking. We find out when something regresses before a client does.
• Build the correction flywheel. Every fix an SMB makes inside a partner’s product is a label. Turn that into measurable accuracy gains.
• Turn written rules into executable logic. Expense policies and approval rules, interpreted, applied, and explainable.
• Partner directly with the founders on where the accuracy bar sits and what we ship next.

• 3+ years shipping applied ML or AI systems into production, not research or analytics.
• Hands-on with LLMs in production: structured output, tool calling, retrieval, prompt and pipeline iteration against measured outcomes.
• Strong production Python. You ship code, not notebooks.
• You’ve built evaluation harnesses and treat accuracy as an engineering problem rather than a judgement call.
• Experience in a domain where being wrong is expensive. Fintech, compliance, legal, insurance, healthcare.
• Genuinely autonomous: take an ambiguous accuracy problem, make the call, ship the improvement.
• Comfortable operating without structure, and creating it.

Bonus: document AI (OCR, layout parsing, entity extraction), AP/AR or accounting systems knowledge, e-invoicing, or time at an API-led fintech or infrastructure company.

• Production-first. If it isn’t live and reliable, it isn’t done.
• Measured, not intuited. If you can’t show the number moved, it didn’t.
• Correctness is non-negotiable. This is someone’s ledger. Precision and auditability come first.
• Pragmatic, not dogmatic. Simple approaches where they work, sophistication only where it earns its keep.
• AI-native. LLMs as infrastructure, not features.
• Built to embed. Multi-tenant from the ground up. Nothing we build should overfit to one client.
• Small team, high trust. Judgement over process, every time.