Lead Product Manager — Pay Later Risk & Credit Strategy

Cleo; Greater London; On-site; GBP 120,000 - 160,000; Full time

Benefits offered by this job

Meaningful equity

Comprehensive health

Dental and vision insurance

Mental health support

Paid sabbatical after four years

Learning & development platform

Pension or retirement contributions

Location-specific leave entitlements

Job summary

Cleo in the United Kingdom is seeking a hands-on Lead Product Manager to drive Cleo’s Pay Later risk decisioning roadmap. You’ll build the risk models, credit limits and policies that decide who qualifies for Pay Later and how much credit they get, leading a cross-functional squad across Engineering, ML and Risk.

Deep fintech risk experience, model literacy across the full ML lifecycle, and a proven ability to move business metrics on a fast‑scaling product are essential.

Qualifications

• Fintech risk/credit/underwriting experience with ML lifecycle literacy.
• Proven track record of moving business metrics on a fast-scaling product.
• Comfort with ambiguity and trade-offs between pricing, limits and credit risk.
• Strong data fluency, runs A/B tests, and can translate data into stories.
• Experience working with legal, compliance and banking partners in the US.

Responsibilities

• Improve risk models through approval, testing and rollout.
• Set credit limits and pricing policy with new risk grids.
• Own bureau data partnerships (TransUnion) and data feeding models.
• Spot and fix data/reporting gaps with central data teams.
• Build solutions to catch fraud within Pay Later.

Skills

Fintech risk

Credit under‑writing

ML lifecycle

Data fluency

SQL

Cross‑functional leadership

Tools

SQL

TransUnion data

Job description

Cleo in the United Kingdom is seeking a hands-on Lead Product Manager to drive Cleo’s Pay Later risk decisioning roadmap. You’ll build the risk models, credit limits and policies that decide who qualifies for Pay Later and how much credit they get, leading a cross-functional squad across Engineering, ML and Risk.

Deep fintech risk experience, model literacy across the full ML lifecycle, and a proven ability to move business metrics on a fast‑scaling product are essential.