Creditspring
Data Scientist - Credit Risk Modelling
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Data Scientist - Credit Risk Modelling
Data Scientist – Credit Risk (Underwriting Analytics) at Creditspring
We are Creditspring, a new way of borrowing that focuses on its members and provides them with safe and efficient short-term financial products.
We're a fast-growing FCA-regulated consumer credit company. We have members, not customers—and we take a lot of pride in that!
As one of the UK’s only subscription finance companies in the market, we truly have a unique value proposition. Our mission is clear: to improve the financial stability and resilience of our members. We achieve this through:
- The products we provide
- The partnerships we have
- Our educational content
We want our members—and the entire UK—to manage their finances better, steering them away from high-cost, unregulated credit sources.
About the Role
We are seeking an experienced and detail-oriented Data Scientist to join our Underwriting – Credit Risk Data Science team, either in our London office or Bengaluru office. This is a mid-level individual contributor role, ideal for someone who thrives on:
- Solving complex problems
- Driving innovation
- Applying advanced analytics and machine learning to real-world business challenges
What You’ll Do
This role will allow you to:
- Influence how the company builds credit risk models
- Monitor model performance and optimise tailored product offerings
- Contribute to production-grade solutions that impact our members’ financial well-being
Sitting at the intersection of Data, Engineering, Operations, Product and Marketing, the role is critical to:
- Supporting platform growth
- Developing fresh lending product innovation
This is a full-stack data science and analytics role, where you’ll spend significant time on:
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- Data extraction
- Wrangling and feature engineering
- Advanced analytics
- Working with Consumer Duty and regulatory compliance
Responsibilities
As a Data Scientist in this role, your focus will span:
-
Model Development & Management
- Ideate and build robust machine learning models for credit risk assessment and adjacent use cases (e.g., collection initiatives, identity resolution, affordability assessment, macro-resilience, and decision explainability).
-
Deployment & Monitoring
- Supervise model deployment by testing and monitoring performance
- Ensure timely calibration and redevelopment
- Identify data/model drift and respond accordingly.
-
Data Pipeline & Tooling
- Contribute to the development and optimisation of data pipelines, tools, and infrastructure.
-
Lending Lifecycle Coordination
- Coordinate changes in the credit lifecycle—from idea generation and solution design to project management, deployment, and continuous monitoring.
-
External API Expertise
- Gain deep insight into external API feeds used for decision-making, including:
- Credit reference agencies
- Open banking data providers
- Alternative data sources.
- Gain deep insight into external API feeds used for decision-making, including:
-
Strategic & Growth Partnerships
- Partner with other teams to assess the feasibility of growth initiatives
- Design and implement strategies for:
- Acquisition
- Product development
- Lending
What You’ll Need to Succeed
We’re looking for an individual with a quantitative background, 3-5 years of experience, and strong expertise in:
Essential Criteria
- Degree in Quantitative Science/Finance/Data Science
- 3–5 years of credit risk analytics experience
- Preference for candidates from SMEs, retail lending, or financial organisations
- Capable of developing and deploying machine learning models (local/cloud environments)
- Knowledge of:
- Regression & gradient boosting techniques
- Model development best practices (hyperparameter tuning, feature engineering, validation, explainability)
- Proficiency with:
- Statistical inference
- Supervised learning (scikit-learn, pandas, numpy)
- Python for:
- Data extraction
- Transformation
- Analysis
- SQL expertise for:
- Data manipulation
- Merging
- Cleaning/comparing across multiple sources (internal + external APIs).
- Strong business acumen
- Clenar communication to influence stakeholders via analytics
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Desirable Experience
- Lending, fintech or regulated-sector work experience
- Hands-on expertise with:
- Open banking data
- Web applications
- Cloud data stacks
- Event-driven architecture
- API-based decisioning SaaS platforms
- Knowledge of Agention AI
You don’t need to meet everything
Research shows that many women and underrepresented groups only apply for jobs when they’re 100% qualified. Here at Creditspring, we’re committed to equity and opportunity. If this role excites you—we’d love to hear from you, even if your experience isn’t a perfect match.
We’re proud to be an inclusive organisation where employees thrive as their authentic selves.
As an equal opportunity employer, we welcome candidates from all backgrounds.
IT's important to note, please only direct your application to our People Team via our dedicated email address: people@creditspring.co
Unsolicited emails sent to any other team members will unfortunately not be received.
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