Data Scientist
Indexed description
The Decision Science team applies statistical modelling, machine learning and emerging AI techniques to:
- Understand, measure and predict customer behaviour across the credit lifecycle
- Optimise collections and customer treatment strategies
- Value portfolios of non‑performing or distressed assets through cash‑flow and cost projections
- Improve operational efficiency and customer outcomes through data‑driven decisioning
Key Outcomes of the Role
The Successful Candidate Will
- Support the growth of the Spanish business, while benefiting from Group‑level standards, tools and expertise
- Deliver hands‑on analytical projects from problem definition through to deployment and monitoring
- Translate complex analysis into actionable, commercially viable recommendations
- Ensure customer outcomes, fairness and experience are central to decision science work
- Become a trusted analytical partner to Spanish stakeholders, while contributing to the wider UK‑based Decision Science team
- Build strong working relationships with Spanish Strategy, Operations, Pricing and Data Engineering teams
- Develop statistical, machine learning and (where relevant) LLM‑based models to agreed objectives and timelines
- Develop deep understanding of Spanish data sources
- Translate analysis into commercially viable and operationally realistic recommendations
- Work with business and technical teams to ensure models are effectively deployed into live decision‑making
- Identify opportunities to enhance modelling approaches and analytical tools used across CCM
- Promote sound analytical methodologies and development standards across projects
- Contribute to realistic project planning and prioritisation
- Support initiatives to improve data quality and analytical capability within the Spanish business
- Coordinate with UK‑based Decision Science colleagues and Data Engineering to support operational implementation
- A creative, structured problem solver
- A confident communicator able to explain complex ideas clearly to non‑technical audiences
- Comfortable working with geographically distributed teams
- Curious, adaptable and motivated to learn
- Degree in a numerate discipline or equivalent professional experience
- Post‑graduate qualification (MSc or PhD) preferred but not essential
- Relevant technical certifications (Python, SQL, cloud platforms, AI/LLMs) are desirable
- Python and SQL for data analysis and modelling
- Regression and tree‑based methods (e.g. linear/logistic regression, GBM, random forest)
- Advanced Excel skills, including data validation and analytical modelling
- Experience with Azure, Databricks or similar cloud platforms
- Exposure to LLMs (prompting, evaluation, or applied use cases)
- Experience working with collections, recoveries or distressed debt data
- Knowledge of GDPR and data governance
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