At a Glance
- Tasks: Design and implement credit risk models to drive responsible lending practices.
- Company: Bits is a fintech startup on a mission to democratise credit and enhance financial futures.
- Benefits: Enjoy flexible work culture, private health insurance, and a learning & development budget.
- Why this job: Join a dynamic team making a real impact on financial inclusion and credit building.
- Qualifications: Strong quantitative background with 5+ years in credit risk modelling and proficiency in Python.
- Other info: Collaborate across departments and stay at the forefront of fintech innovations.
The predicted salary is between 43200 - 72000 £ per year.
About BitsAt Bits, we\’re on a mission to democratize credit and build a fairer financial future. We help customers across the UK and beyond to build their credit profile through smart, responsible financial tools. As we scale our operations and launch new products, we\’re expanding our Risk element with a key technical hire who will shape the future of our credit strategy and help us manage risk intelligently.
About the RoleWe are looking for a skilled and analytical Credit Risk modeller to join our growing team. In this role, you will design, implement, and maintain robust credit risk models that are critical to Bits\’ decision‐making framework. You\’ll play a central role in model governance, regulatory compliance, and translating complex data into actionable business insights.
This is a high‐impact role for someone with a deep understanding of credit risk, strong quantitative skills, and a passion for driving responsible lending practices in a fast‐paced fintech environment.
Key responsibilities1. Model Development and Implementation
Develop, calibrate, and deploy credit risk models across the customer lifecycle, with a focus on acquisition and behavioural models such as application scorecards, early warning signals, and delinquency/default prediction models – using advanced statistical and machine‐learning techniques.
Lead end‐to‐end model lifecycle from initial data exploration to production deployment.
2. Data Analysis and Interpretation
Apply advanced statistical and data mining techniques to extract meaningful insights from large and complex datasets.
Identify trends and risk drivers to inform and improve model accuracy and predictability.
3. Credit Strategy & Portfolio Growth
Play a key role in leveraging these models to shape data‐driven credit strategies that drive business growth, improve customer retention, and optimise revenue across the portfolio.
4. Monitoring and Maintenance
Regularly monitor model performance and recalibrate as necessary to reflect portfolio and macroeconomic changes.
Maintain thorough documentation and version control for all models.
5. Stakeholder Communication
Communicate technical findings clearly to both technical and non‐technical stakeholders, including senior leadership and regulatory bodies.
Provide thought leadership on model risk and credit strategy.
6. Cross‐Functional Collaboration
Work closely with other departments – Product, Engineering, and Compliance teams to ensure models are integrated effectively into decision engines and business processes.
7. Research & Innovation
Stay abreast of industry trends, new modelling techniques, and regulatory developments.
Recommend enhancements to existing models and explore new approaches.
Qualifications
Strong academic background in a quantitative field (e.g., Mathematics, Statistics, Engineering, Computer Science).
5+ years of experience building and validating credit risk models in a financial services or fintech environment.
Proficiency in both traditional statistical methods (e.g. logistic regression) and modern machine‐learning approaches (e.g. XGBoost, random forests) for predictive modeling and risk segmentation.
Proficiency in Python for statistical modeling and data analysis.
Strong SQL skills and experience working with large datasets.
Proven ability to work with credit bureau and open banking data to design predictive models and shape data‐driven acquisition and customer management strategies.
Experience with regulatory frameworks such as Basel and IFRS 9 is a plus, particularly in the context of credit risk modeling.
Excellent written and verbal communication skills with the ability to influence stakeholders at all levels.
Familiarity with model governance, validation, and documentation best practices.
Benefits
A dynamic and inclusive work environment in a rapidly growing fintech startup.
Opportunities for professional development and career growth.
The chance to make a significant impact on financial inclusion and credit building for underserved communities.
Make an impact in a mission‐led company redefining how people build credit.
Work with a smart, supportive, and driven team at the cutting edge of fintech.
Private health insurance, L&D budget, and other great perks.
Competitive salary.
Competitive salary and equity options.
Flexible work culture with hybrid setup.
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Credit Risk Modeller employer: Bits
Contact Detail:
Bits Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Credit Risk Modeller
✨Tip Number 1
Familiarise yourself with the latest trends in credit risk modelling and fintech. This will not only help you understand the industry better but also allow you to engage in meaningful conversations during interviews, showcasing your passion and knowledge.
✨Tip Number 2
Network with professionals in the credit risk and fintech sectors. Attend relevant meetups, webinars, or conferences to connect with potential colleagues and learn about the challenges they face, which can give you insights to discuss during your interview.
✨Tip Number 3
Brush up on your technical skills, especially in Python and SQL, as these are crucial for the role. Consider working on personal projects or contributing to open-source projects that involve credit risk modelling to demonstrate your capabilities.
✨Tip Number 4
Prepare to discuss your previous experiences with model governance and regulatory frameworks. Be ready to provide examples of how you've successfully navigated these areas in past roles, as this will be key to showing your fit for the position.
We think you need these skills to ace Credit Risk Modeller
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in credit risk modelling and quantitative analysis. Use specific examples of models you've developed or worked on, particularly those that align with the responsibilities outlined in the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for responsible lending practices and how your skills can contribute to Bits' mission. Mention your familiarity with regulatory frameworks and your ability to communicate complex data insights to various stakeholders.
Showcase Technical Skills: Clearly demonstrate your proficiency in Python, SQL, and statistical methods in both your CV and cover letter. Provide examples of how you've applied these skills in previous roles, especially in developing predictive models.
Highlight Collaboration Experience: Since the role involves cross-functional collaboration, include examples of how you've successfully worked with other departments in past positions. This could involve working with product teams or compliance to integrate models into business processes.
How to prepare for a job interview at Bits
✨Showcase Your Technical Skills
Be prepared to discuss your experience with credit risk modelling, including specific techniques you've used like logistic regression or machine learning methods. Bring examples of models you've developed and be ready to explain the thought process behind them.
✨Understand the Business Context
Research Bits and its mission to democratise credit. Be ready to discuss how your work in credit risk modelling can directly contribute to their goals, such as improving customer retention and optimising revenue.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to communicate findings to both technical and non-technical stakeholders, so demonstrating your ability to bridge that gap will be crucial.
✨Prepare for Scenario Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about how you would approach model recalibration in response to macroeconomic changes or how to integrate models into existing business processes.