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# AI Data Scientist

- **Company:** Dolfin AI
- **Location:** Greater London, UK (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Accounting Systems, Application Programming Interfaces (APIs), Artificial Intelligence, Software as a Service, Python (Programming Language), Named Entity Recognition, Parsing, Large Language Models, Api Design
- **Published:** September 9, 2026
- **Apply:** https://www.collegerecruiter.com/job/2856358386-ai-data-scientist

## About the Role

* 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.

## Description

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.


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