- 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
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Senior Data Scientist – Credit Risk Modelling employer: Jobtailor
As a Client Services Coordinator at our dynamic company, you will thrive in a supportive work culture that prioritises employee growth and development. We offer comprehensive training, opportunities for advancement, and a collaborative environment where your contributions are valued. Located in a vibrant area, our team enjoys a healthy work-life balance and the chance to engage with diverse clients, making every day rewarding and meaningful.
