Senior Data Scientist - Credit Risk Modelling
I
iwocaCredit risk
Hybrid in London or remote in the UKFull-TimeSenior
Salary£90K - £130K; £90K – £130K
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Job Details
- Experience
- You've applied it to commercial problems for several years
- Required Skills
- Python
Requirements
- Have a background in statistics from a quantitative field, typically a degree or Master's in Statistics, Mathematics, Physics, or similar.
- Have several years of experience applying statistics to commercial problems and choosing methods independently.
- Understand when data violates a method’s assumptions and how that affects results.
- Quantify uncertainty in estimates and treat model calibration as a first-order concern.
- Have built supervised machine-learning models end to end, including exploration, training, deployment, and monitoring.
- Critically evaluate model outputs and explain and defend modelling choices with evidence.
- Have led self-identified modelling projects from framing the question through to a business decision.
- Have used AI to produce work that would otherwise not have been deliverable and built tools or workflows used by colleagues.
- Write and speak clearly, directly, and concisely, adapting technical detail to the audience.
- Python coding for analysis is used by the team.
Responsibilities
- Own credit risk and Customer Lifetime Value (CLtV) modelling projects end to end, from identifying where models constrain the business to getting changes live.
- Set the technical direction for multi-quarter modelling projects.
- Keep existing models accurate as conditions change and develop incremental improvements.
- Research modelling approaches that could replace existing models.
- Estimate the causal effects of offer amount, duration, and price on customer outcomes.
- Develop a common cost function for automated and manual model families.
- Develop a multi-stage IFRS accounting model in which recovery predictions improve upfront loss estimates.
- Explore whether a general model could replace separate credit and CLtV models.
- Review other data scientists’ modelling choices and coach newer team members.
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