Principal Data Scientist (Fintech)

TL;DR

Principal Data Scientist (Fintech): Defining and owning the data science strategy for non-lending products, including car insurance, with an accent on strategic roadmap development and stakeholder influence. Focus on driving ML projects from scoping to deployment and mentoring the analytics community.

Location: Hybrid (London) - Work from home every Monday and Friday; in-office Tuesday through Thursday.

Company

hirify.global is a profitable fintech unicorn focused on building world-class technology to help people access credit and save money.

What you will do

• Own the data science roadmap to support the expansion into non-lending products.
• Partner with managing directors to define requirements and success metrics for DS projects.
• Drive data science initiatives from strategic planning through to tactical execution.
• Build trust and influence senior leaders and a diverse range of stakeholders.
• Communicate complex technical results effectively via written and verbal channels.
• Mentor a team of talented data scientists and contribute to 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, including typical ML applications and regulations.
• Strong technical proficiency in Python, SQL, and statistics.
• Hands-on experience across the full model lifecycle: scoping, development, deployment, and monitoring.
• Must be based in London for a hybrid work arrangement.

Nice to have

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

Culture & Benefits

• Best-in-class compensation package including equity.
• Flexible hybrid model with designated remote days (Monday and Friday).
• Fresh, healthy lunches prepared by in-house chefs in the office (Tue-Thu).
• Private health insurance coverage.
• Inclusive and open workspace culture.

Hiring process

• Recruiter introductory call.
• Hiring Manager interview.
• Technical interview including a case study.
• Final interviews with the Head of Data Science and Chief Risk Officer.