Data Scientist (Fintech)

TL;DR

Data Scientist (Fintech): Developing machine learning models for underwriting, pricing, and customer management for the UK credit cards business with an accent on growth, risk, and profitability. Focus on building quantitative frameworks for high-dimensional datasets and overseeing the full model lifecycle in production.

Location: Hybrid (London, UK). Must be able to work from the office Tuesday through Thursday.

Company

hirify.global is a fintech unicorn building advanced technology to optimize credit access and savings for customers in the UK and US markets.

What you will do

• Develop and deploy machine learning models to improve underwriting, pricing, and customer management.
• Analyze large-scale, high-dimensional datasets to generate insights for credit and commercial strategy.
• Take full ownership of models throughout their lifecycle, from research and development to production monitoring.
• Collaborate with product, engineering, and risk teams to translate ambiguous business problems into measurable outcomes.
• Contribute to the team's technical growth through experimentation and knowledge sharing.

Requirements

• Strong programming skills in Python and SQL.
• Deep understanding of statistics, machine learning, and quantitative problem solving.
• Ability to approach complex, ambiguous problems with a pragmatic and structured mindset.
• Must be based in London or able to work from the London office 3 days a week.

Nice to have

• Experience in credit risk, lending, or the broader financial services sector.
• Interest in advanced machine learning research and applied techniques.
• Interest in machine learning infrastructure and optimization.

Culture & Benefits

• Best-in-class compensation, including equity.
• Hybrid work model with remote flexibility every Monday and Friday.
• Daily fresh, healthy lunches prepared by in-house chefs (Tuesday-Thursday).
• Private health insurance coverage.
• Inclusive and open workspace culture.

Hiring process

• Initial recruiter call.
• Take-home technical task.
• Interviews with the Hiring Manager and Technical lead.
• Case study and final interview.