Senior Data Scientist – Credit Risk Modelling

Jobtailor; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Jobtailor in Greater London is seeking an experienced data scientist specializing in credit risk and customer lifetime value modelling. You will own end-to-end modelling projects, frame opportunities, and guide the technical direction across multi-quarter programmes.

You will monitor production models, drive incremental research, and communicate commercial impact to stakeholders. A background in probability and statistics and hands-on experience with gradient boosting, neural networks, and

Qualifications

• Background in probability and statistics from a quantitative field.
• Experience building and shipping supervised machine-learning models end to end.
• Research mindset and proactive exploration of new ways to add value.
• Ability to critically evaluate model outputs and defend technical reasoning.
• Experience influencing technical direction beyond own projects.
• Experience owning modelling projects end to end, from opportunity identification through commercial impact.
• Ability to move quickly, iterate, and update based on new evidence.
• AI fluency, including using AI 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 shipping gradient boosting or neural networks on tabular data in production 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.

Responsibilities

• Own credit and Customer Lifetime Value modelling projects end to end.
• Identify where the modelling stack is holding the business back.
• Frame modelling opportunities and select appropriate methods.
• Keep production models healthy through monitoring and maintenance.
• Deliver incremental model development and research that reshapes model approaches.
• Set technical direction on multi-quarter projects.
• Develop principled approaches to unify auto and manual credit models.
• Contribute to the IFRS accounting model and multi-stage credit modelling.
• Investigate generalized approaches that could replace separate credit and CLtV models.
• Land commercial impact and communicate technical decisions.
• Collaborate with a team of approximately twelve data scientists.

Skills

Credit Modelling

Customer Lifetime Value Modelling

Supervised Machine-Learning

AI Fluency

Technical Communication

Education

Quantitative field degree

Tools

Python

Job description

• Own credit and Customer Lifetime Value modelling projects end to end
• Identify where the modelling stack is holding the business back
• Frame modelling opportunities and select appropriate methods
• Keep production models healthy through monitoring and maintenance
• Deliver incremental model development and research that reshapes model approaches
• Set technical direction on multi-quarter projects
• Develop principled approaches to unify auto and manual credit models
• Contribute to the IFRS accounting model and multi-stage credit modelling
• Investigate generalised approaches that could replace separate credit and CLtV models
• Land commercial impact and communicate technical decisions
• Collaborate with a team of approximately twelve data scientists

Requirements

• Background in probability and statistics from a quantitative field
• Experience building and shipping supervised machine-learning models end to end, including exploration, training, deployment, and monitoring
• Research mindset and proactive exploration of new ways to add value
• Ability to critically evaluate model outputs and defend technical reasoning
• Experience influencing technical direction beyond own projects
• Experience owning modelling projects end to end, from opportunity identification through commercial impact
• Ability to move quickly, iterate, and update based on new evidence
• AI fluency, including using AI 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 shipping gradient boosting or neural networks on tabular data in production 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 and Customer Lifetime Value modelling, with a strong background in probability and statistics. Proficient in building and deploying supervised machine-learning models, while effectively communicating technical decisions and collaborating with data science teams.

Highest-signal resume keywords

• Credit Modelling
• Customer Lifetime Value Modelling
• Supervised Machine-Learning
• AI Fluency
• Technical Communication

Hard Skills

• Probability
• Statistics
• Model Development
• Gradient Boosting
• Neural Networks
• Hierarchical Models
• MCMC
• Bayesian Updating
• Temporal Data Modelling
• Python

Soft Skills

• Research Mindset
• Critical Evaluation
• Proactive Exploration
• Clear Communication
• Collaboration

Industry Keywords

• Credit Risk
• Lending
• Commercial Impact
• Model Monitoring
• Model Maintenance