IT & Software

Data Scientist - iwocaPay Risk Squad

iwoca

London · Greater London · United Kingdom

Overview

In this role you will co-own the credit modelling stack for iwocaPay, shaping risk models that determine funding access and terms for SMEs. You’ll work in a small, growing risk squad that values end-to-end model development and commercial impact. The role blends production ML, risk analytics and collaboration with product and risk teams to drive responsible growth. This is a hands-on opportunity to influence lending decisions and product scale in a fast-moving fintech environment.

Pay / Benefits
  • Flexible working hours
  • Medical insurance from Vitality
  • Private GP service for you and dependents
  • 25 days holiday + birthday day, extra days and sabbatical
  • Pension contributions
  • Employee equity incentive scheme
Responsibilities
  • Co-own the full credit modelling stack with another data scientist
  • Collaborate on model design and prioritize work based on impact
  • Build, deploy, and monitor end-to-end production models for credit decisions
  • Influence risk and product strategy through analytical insights
  • Maintain and improve models across segments with an eye on commercial outcomes
Key requirements
  • Supervised machine learning on tabular data, end-to-end production experience
  • Strong foundations in probability, uncertainty quantification, calibration
  • Ability to scope problems, build infrastructure, and ship models in an early-stage setting
  • Pragmatic with business constraints while maintaining rigor on high-stakes models
  • Data engineering capability to build pipelines and manage datasets
  • AI fluency to automate and accelerate analytical work
  • Clear communication to non-technical colleagues in product, risk, and leadership
  • independent working in ambiguous environments
  • pragmatism balanced with rigor
  • clear and adaptable communication
  • production-grade models for tabular data
  • probability and uncertainty quantification
  • model calibration and error analysis

Reference: WJ-747_30138749

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