Data Science Manager (Fintech)

TL;DR

Data Science Manager (Fintech): Owning the data science strategy and building a team for non-lending products with an accent on commercial value and strategic delivery. Focus on hiring and developing talent, defining project success, and driving ML initiatives across the full model lifecycle.

Location: Hybrid (London). Must be based in London and available to work from the office Tuesday through Thursday.

Company

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

What you will do

• Build, develop, and retain a high-performing data science team for non-lending products.
• Own the data science roadmap to drive new product expansion.
• Partner with managing directors to define strategic objectives and lead project delivery.
• Gather internal and external requirements to define project success metrics.
• Mentor data scientists and contribute to the broader analytics community.
• Communicate results and strategic insights to senior stakeholders.

Requirements

• 2+ years of experience in a people management role.
• Proven track record of managing data science projects with cross-functional teams.
• Strong technical skills in Python, SQL, and statistics.
• Extensive knowledge of the financial services industry, including ML applications and regulations.
• Experience across the entire model lifecycle from scoping to deployment and monitoring.
• Must be based in London to support the hybrid work model.

Nice to have

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

Culture & Benefits

• Hybrid work arrangement with remote Mondays and Fridays.
• Best-in-class compensation including equity.
• Fresh, healthy lunches prepared by in-house chefs in the office (Tue-Thu).
• Private health insurance for physical and mental well-being.
• Inclusive and open workspace environment.

Hiring process

• Recruiter call followed by an initial call with the Hiring Manager.
• Technical interview and a case study.
• Final interviews with the Head of Data Science and Chief Risk Officer.