Data Scientist - Credit, ML & AI

Fairlo•Stockholm, ME

1d•Hybrid

About The Position

Fairlo is a fintech operating in Sweden and the UK, helping people access sustainable finances through transparent and flexible credit. The company is growing as it works toward becoming a bank and is using AI to support fairer credit decisions, faster underwriting, and better customer outcomes. The Data Scientist will join the Credit team as it brings ownership of Fairlo’s credit decision engine fully into the team. The role owns models, data parsers, and AI tooling used to decide who receives credit and at what amount across two regulated markets. The team uses AI coding agents and large language models to extract useful signals from unstructured financial information, including bank statements, open banking transactions, and customer documents. The role covers end-to-end work, from initial analysis through production deployment and monitoring. It involves building data pipelines and features; developing and validating machine-learning models for risk, affordability, and credit-limit decisions; evaluating LLM-powered data extraction; using AI-assisted engineering tools; and ensuring models are explainable, fair, monitored, and ready for regulatory scrutiny. The position works closely with risk, product, and engineering teams. The role is based mostly in Fairlo’s Stockholm office under a hybrid working model, with occasional travel to the company’s tech hub in Crete and the UK.

Requirements

Fluency in Python and SQL.
Experience taking machine-learning models into production, not just working in notebooks.
Hands-on daily use of AI coding tools such as Claude Code, Cursor, Snowflake Cortex, or similar, with informed views on where they are useful and where they are not.
Experience building with LLMs, including prompting, structured extraction, retrieval, or evaluation.
Experience running software in AWS or a strong desire to learn.

Nice To Haves

Experience in banking, credit, or another regulated industry.
Knowledge of model risk management, explainability, or fairness testing.
Familiarity with the EU AI Act and its classification of credit scoring as high-risk.

Responsibilities

Own projects end-to-end, from the first query through production and monitoring.
Analyse large datasets, engineer features, and build robust data pipelines.
Develop, validate, and improve machine-learning models for risk, affordability, and credit-limit decisions.
Use large language models to parse and categorise unstructured financial data, and build evaluation sets to verify performance.
Use AI coding agents such as Claude Code and Cursor to work more efficiently and help the team adopt effective tools.
Keep models explainable, fair, and monitored, and prepare them for regulators and auditors.
Use CI/CD for testing, deployment, and monitoring of models in AWS.
Collaborate with risk, product, and engineering teams to break complex problems into shippable steps.