Data Scientist - External Data Strategy
Summary: The Data Scientist - External Data Strategy role involves evaluating and optimizing external data sources for applications in credit risk, fraud prevention, collections, and customer decision-making. The position requires analytical insights to drive strategic data investment decisions, focusing on the value of various data sources. The ideal candidate will have a strong background in data science and quantitative analytics, particularly within financial services or fintech. This role is suited for a data-driven professional who can effectively communicate complex findings to stakeholders.
Key Responsibilities:
- Evaluate new external data sources and quantify their business value.
- Measure incremental lift and marginal contribution versus existing data signals.
- Build performance monitoring frameworks to assess ongoing effectiveness.
- Analyse coverage gaps and identify opportunities to improve customer decisioning.
- Partner with Data Science, Credit Risk, Fraud, Product, and Vendor Management teams.
Key Skills:
- 3+ years' experience in Data Science, Quantitative Analytics, Credit Risk, or a similar analytical role within Financial Services or Fintech.
- Strong Python and SQL skills.
- Experience building and evaluating Machine Learning models.
- Proven ability to measure incremental lift, model performance, or business impact.
- Knowledge of credit risk concepts, including bureau data, segmentation, approval rates, and loss trade-offs.
- Excellent communication skills with the ability to present complex findings to non-technical stakeholders.
- Comfortable working in a fast-paced environment with ambiguity and evolving priorities.
Salary (Rate): £500 daily
City: London
Country: UK
Working Arrangements: undetermined
IR35 Status: inside IR35
Seniority Level: undetermined
Industry: IT
Detailed Description From Employer:
Data Scientist - External Data Strategy
Join a high-impact team responsible for evaluating and optimising external data sources used across credit risk, fraud prevention, collections, and customer decisioning. You will assess the value of credit bureau, open banking, and alternative data sources, providing the analytical insight needed to drive strategic data investment decisions.
Key Responsibilities
- Evaluate new external data sources and quantify their business value.
- Measure incremental lift and marginal contribution versus existing data signals.
- Build performance monitoring frameworks to assess ongoing effectiveness.
- Analyse coverage gaps and identify opportunities to improve customer decisioning.
- Partner with Data Science, Credit Risk, Fraud, Product, and Vendor Management teams.
Essential Requirements
- 3+ years' experience in Data Science, Quantitative Analytics, Credit Risk, or a similar analytical role within Financial Services or Fintech.
- Strong Python and SQL skills.
- Experience building and evaluating Machine Learning models.
- Proven ability to measure incremental lift, model performance, or business impact.
- Knowledge of credit risk concepts, including bureau data, segmentation, approval rates, and loss trade-offs.
- Excellent communication skills with the ability to present complex findings to non-technical stakeholders.
- Comfortable working in a fast-paced environment with ambiguity and evolving priorities.
Desirable Experience
- Credit bureau data (Experian, Equifax, TransUnion, CRIF, Schufa, etc.).
- Open banking data (Plaid, Tink, Finicity, etc.).
- International credit market exposure, particularly across the UK and Europe.
- Vendor economics, data pricing, or cost-benefit analysis.
- Analytical sandbox environments.
- Master's degree or higher in Statistics, Economics, Computer Science, Mathematics, or a related quantitative field.
Ideal for a data-driven professional who enjoys combining analytics, strategy, and business impact to shape critical decision-making capabilities.
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