Director of Data Science for Payment Performance — Hybrid, London, United Kingdom

Overview

Lead a data science organization working on the decisions that determine payment acceptance, fraud exposure and processing cost. The position brings fraud and acceptance specialists behind a shared strategy while preserving the methods and domain knowledge needed for different payment problems.

Requirements

• Candidates need a strong record of leading teams that deliver real-time or large-scale machine-learning products.
• Applied modeling judgment, rigorous experimentation and experience turning models into reliable services are essential.
• Payments fraud, authorization, routing or another live decisioning domain provides useful context.
• The assessment emphasizes demonstrated system and team impact rather than a fixed number of years.

Responsibilities

• Set priorities across models, data and experimentation in partnership with product and engineering leaders.
• Own the scientific lifecycle from defining a problem through evaluation, production launch and continuing performance.
• Judge progress through merchant and business outcomes, including successful transactions and avoided losses, rather than relying only on offline model measures.
• Maintain direct technical involvement through design reviews, selected analyses and prototypes.
• Improve explainability, monitoring, drift detection and model iteration, and work with engineering on low-latency deployment capabilities.
• The leadership remit includes coaching a distributed team, clarifying ownership, hiring scientists and developing future technical or people leaders.

Conditions

• The position is full time and hybrid in London, with three office days per week and occasional travel for team and product planning.