Lead Data Scientist (Fintech)

TL;DR

Lead Data Scientist (Fintech): Defining and leading the data science strategy for non-lending products, such as car insurance, with an accent on strategic roadmap ownership and cross-functional leadership. Focus on building high-frequency automated systems, managing the full ML model lifecycle, and unlocking commercial value through data-driven decisions.

Location: Hybrid (London). Office attendance required Tuesday through Thursday; remote work allowed on Mondays and Fridays.

Company

A fintech unicorn on a mission to build the world's best technology to help people get credit and save money.

What you will do

• Own the data science roadmap to facilitate the expansion into new products, starting with Car Insurance.
• Partner with managing directors to define and deliver strategic data science projects.
• Gather internal and external requirements to establish success metrics for data science initiatives.
• Drive the end-to-end model lifecycle, including scoping, development, deployment, and monitoring.
• Communicate complex results to senior stakeholders and mentor the broader analytics community.

Requirements

• Proven track record of managing data science projects with cross-functional teams and senior stakeholders.
• Extensive knowledge of the financial services industry, its data, ML applications, and regulations.
• Strong technical skills in Python, SQL, and statistics.
• Hands-on experience across the entire ML model lifecycle from scoping to monitoring.
• Must be based in London to support the hybrid office schedule.

Nice to have

• Experience working in insurance, telco, or financial services consulting.

Culture & Benefits

• Flexible hybrid model with remote work on Mondays and Fridays.
• In-house chefs providing fresh, healthy lunches in the office (Tue-Thu).
• Best-in-class compensation package, including equity.
• Private health insurance coverage.
• Commitment to an inclusive and open workspace in London.

Hiring process

• Initial recruiter call and cognitive assessment.
• Hiring Manager and technical interviews, including a case study.
• Final discussions with the Head of Data Science and Chief Risk Officer.