At a Glance
- Tasks: Design and build credit risk models using modern techniques to drive real business decisions.
- Company: Join a fast-growing, innovative company with a friendly and inclusive culture.
- Benefits: Competitive pay, bonuses, hybrid working, and opportunities for professional growth.
- Other info: Be part of a diverse team committed to inclusivity and continuous improvement.
- Why this job: Make an impact in the credit domain while developing your data science skills.
- Qualifications: 3+ years in data science, strong Python and SQL skills, and a passion for building models.
The predicted salary is between 50000 - 60000 £ per year.
The role of Data Scientist (Credit Risk) is important to us. They will be responsible for designing, building, validating and maintaining the credit risk models that will sit at the heart of how Radius makes credit decisions. The role will also share responsibility for data modelling and engineering within the credit department. We will be happy to welcome you to our office in Crewe, with working hours from Monday to Friday from 8.30 am to 5.30 pm.
Your daily responsibilities will be:
- Design, build and validate credit risk models – application scorecards, behavioural scorecards, recovery propensity and lifetime-value models: using modern techniques such as gradient boosting and survival analysis.
- Translate credit and commercial questions into clear modelling problems, and turn model outputs into decisions the business can act on.
- Validate model performance and design monitoring so models stay accurate as the portfolio changes (for example Gini / CAP tracking and population stability).
- Work hands‑on with raw data from multiple source systems, shaping it into clean, model‑ready datasets.
- Partner closely with the Head of Credit Insights, the wider analytics team, Finance and Commercial to make sure models drive real credit decisions.
- Document methodology clearly and explain technical work to non‑technical audiences.
Qualifications:
- At least 3 years in a data science or quantitative modelling role, with a track record of building and validating predictive models.
- Strong Python and SQL, with practical experience across classification, regression and ideally survival analysis.
- Confidence working with messy, real‑world data drawn from multiple source systems.
- A clear communicator who can explain models, and their limitations, to a business audience.
- Genuine curiosity about the credit domain and a drive to build, not just analyse.
Additional Information:
- A friendly culture that reflects our proposition to customers.
- A fast‑growing organization that defines itself as agile and innovative.
- A drive for continuous improvement, which will give you the opportunity to support from day one.
- A commitment to building a work environment that values inclusivity, innovation, agility and grit.
- Competitive pay, a potential bonus, and a good range of basic benefits including hybrid working, real autonomy and the chance to help build a new data science capability within Credit from the ground up.
Diversity, Equality & Inclusion at Radius:
Our global DEI networks champion LGBTQ+ inclusion, cultural diversity, women’s empowerment and mental health, neurodiversity and disability support. Radius is an equal opportunities employer. We are committed to welcome people regardless of age, disability, gender identity, race, faith or belief, sexual orientation or socioeconomic background. We are committed to ensuring an inclusive and accessible recruitment process for all candidates. If you require any adjustments or accommodations at any stage of the process, please let us know, and we will do our best to support you.
Data Scientist (Credit Risk) in Crewe employer: Radius
At Radius, we pride ourselves on being an excellent employer, offering a vibrant and inclusive work culture in Crewe that fosters innovation and continuous improvement. As a Data Scientist (Credit Risk), you will enjoy competitive pay, hybrid working options, and the opportunity to shape our new data science capability from the ground up, all while collaborating with a diverse team committed to making impactful credit decisions.
StudySmarter Expert Advice🤫
We think this is how you could land Data Scientist (Credit Risk) in Crewe
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We think you need these skills to ace Data Scientist (Credit Risk) in Crewe
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Radius, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Radius. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Radius
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
