Data Scientist – Credit Risk Modelling

iwoca; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

Jobtailor in London seeks a quantitative expert to advance credit risk and customer lifetime value models, combining probabilistic reasoning with AI-driven prototyping. You will work closely with risk teams to shape models through research and experimentation, linking theory to practical lending decisions.

You will contribute to end-to-end model development, calibration, and governance, ensuring robust performance across scenarios and regulatory standards in a fast-paced fintech setting.

Qualifications

• Background in probability and statistics from a quantitative field.
• Ability to reason about uncertainty and calibration as first-order concerns.
• Active research mindset and interest in exploring new ways to add value.
• Ability to critically evaluate model output and explain and defend reasoning.
• Ability to take ambiguous analytical problems end to end, from framing to a landed decision.
• Ability to use AI as a primary tool for prototyping, automation, and R&D.
• Clear, direct written and verbal communication.

Responsibilities

• Run credit and customer lifetime value modelling projects alongside the Credit Risk Modelling team.
• Keep production models healthy.
• Develop models incrementally.
• Conduct research that reshapes how credit and customer lifetime value models work.
• Model how offer amount, duration, and price shape customer outcomes through causal estimation.
• Work on a multi-stage IFRS accounting credit model that propagates later-stage recovery predictions into upfront loss estimates.
• Explore whether a generalised framing could replace separate credit and customer lifetime value models.
• Support fully automated and human-in-the-loop lending decisions.

Skills

Critical Thinking

Communication

Problem Solving

Analytical Mindset

Collaboration

Tools

AI Tools

Statistical Software

Job description

Job Responsibilities

• Run credit and customer lifetime value modelling projects alongside the Credit Risk Modelling team


• Keep production models healthy


• Develop models incrementally


• Conduct research that reshapes how credit and customer lifetime value models work


• Model how offer amount, duration, and price shape customer outcomes through causal estimation


• Work on a multi-stage IFRS accounting credit model that propagates later-stage recovery predictions into upfront loss estimates


• Explore whether a generalised framing could replace separate credit and customer lifetime value models


• Support fully automated and human-in-the-loop lending decisions


• Interface with and support smaller analytic functions across the business

Requirements

• Background in probability and statistics from a quantitative field


• Ability to reason about uncertainty and calibration as first-order concerns


• Active research mindset and interest in exploring new ways to add value


• Ability to critically evaluate model output and explain and defend reasoning


• Ability to take ambiguous analytical problems end to end, from framing to a landed decision


• Ability to use AI as a primary tool for prototyping, automation, and R&D


• Clear, direct, and concise written and verbal communication


• Domain experience in credit risk, lending, or customer lifetime value modelling is a bonus


• Experience building and shipping supervised machine-learning models end to end is a bonus


• Understanding of model cost functions and inductive biases is a bonus


• Experience with hierarchical models, MCMC, or Bayesian updating is a bonus


• Experience modelling temporal data involving autocorrelation, drift, or seasonality is a bonus


• Python experience is a bonus

Core Competencies

Demonstrates expertise in credit risk and customer lifetime value modeling, utilizing probability and statistics to develop and evaluate models. Proficient in using AI for prototyping and automation while effectively communicating complex analytical concepts.

Highest-signal resume keywords

• Credit Risk Modelling


• Customer Lifetime Value Modelling


• Machine Learning Model Development


• Causal Estimation


• Python Programming

Hard Skills

• Probability


• Statistics


• Model Evaluation


• Hierarchical Models


• MCMC


• Bayesian Updating


• Temporal Data Modelling


• Inductive Biases


• Automation


• Research Methodology

Soft Skills

• Critical Thinking


• Communication


• Problem Solving


• Analytical Mindset


• Collaboration

Industry Keywords

• Lending


• Credit Risk


• Customer Outcomes


• IFRS Accounting


• Model Cost Functions

Tools & Technologies

• AI Tools


• Statistical Software