At a Glance
- Tasks: Analyse data to detect fraud and develop machine learning models.
- Company: Join a forward-thinking company focused on innovation and inclusivity.
- Benefits: Competitive salary, diverse work environment, and opportunities for growth.
- Other info: Collaborative team culture with a focus on diversity and inclusion.
- Why this job: Make a real impact in fraud prevention while working with cutting-edge technology.
- Qualifications: Experience in fraud analytics and machine learning; strong Python and SQL skills.
The predicted salary is between 45000 - 55000 £ per year.
We are currently looking for an experienced and detail-oriented applied data science and business analyst to join our Underwriting data science team with primary focus on fraud detection and mitigation. This is a mid-level applied or “full-stack” data scientist role, ideal for someone with good command of the analytical and machine learning toolkit and desire to drive process and systems change based on the gained insights. You will be instrumental in shaping the company’s fraud prevention initiatives using internal and external data, developing and implementing fraud detection models and providing monitoring and analytics in this area. This role will collaborate extensively with colleagues from across the business (Data, Engineering, Underwriting, Operational Risk and Product teams), and is critical to support further platform growth and credit product innovation.
Responsibilities
- Collect, process and analyse large and complex internal and external datasets to identify trends, risks and opportunities.
- Design, develop and maintain fraud scoring, identity resolution and credit scoring machine learning models.
- Interact with new and existing datasets and solutions providers to run retro analysis, A/B testing and POC exercises.
- Review and test applicability of latest developments in fraud modelling to company’s operations (graph and network analytics, behavioural biometrics, real‑time detection, adversarial thinking, AI agent networks and other techniques).
- Testing and integration of external API feeds into decisioning flow.
- Monitoring, reporting and visualisation of insights and performance metrics.
- Cross‑team collaboration on incoming queries related to Fraud, AML and KYC verification cases.
What you’ll need to succeed
- Prior experience in fraud prevention analytics, preferably within an SME or retail lending environment.
- Experience developing and deploying machine learning models in a local and cloud environment.
- Strong command of statistical inference and supervised machine learning stack (scikit‑learn, pandas, numpy, jupyter).
- Solid knowledge of Python for data extraction, transformation and analysis.
- SQL proficiency for working with data from multiple sources including internal data and external feeds.
- Demonstrated success in systems integration and analytics delivery.
- Commercial awareness with strong communication skills and the ability to influence stakeholders.
Nice to have
- Lending, fintech and regulated sectors work experience.
- Working with web applications, cloud data stacks and event‑driven architecture (we run on ruby on rails, python, aws, github).
- Hands‑on working with credit bureau and open banking data.
- First‑hand experience with decisioning SaaS platforms or AI agents.
As an equal opportunities employer, we know that diversity is a key part of our teams’ successes – so if your experience doesn’t fit perfectly but this role excites you, we’d love for you to apply. We’re committed to Creditspring being an inclusive environment where employees feel welcomed, valued and listened to; we want you to thrive as your true self.
Applied Data Scientist – Fraud Prevention employer: Creditspring
At Creditspring, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation. Our commitment to employee growth is evident through continuous learning opportunities and a supportive environment where diverse perspectives are valued. Located in a vibrant area, our team enjoys the unique advantage of working at the forefront of fraud prevention technology while contributing to meaningful change in the financial sector.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Data Scientist – Fraud Prevention
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who works in fraud prevention. Building relationships can open doors that job applications alone can't.
✨Show Off Your Skills
When you get the chance to chat with potential employers, don’t hold back! Share your experiences with machine learning models and how you've tackled fraud detection challenges. Real-life examples can make you stand out from the crowd.
✨Tailor Your Approach
Every company is different, so do your homework! Understand their fraud prevention strategies and be ready to discuss how your skills can help them improve. This shows you're genuinely interested and not just sending out generic applications.
✨Apply Through Our Website
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re keen on joining our team and contributing to our fraud prevention initiatives.
We think you need these skills to ace Applied Data Scientist – Fraud Prevention
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Applied Data Scientist – Fraud Prevention. Highlight your experience with fraud analytics and machine learning models, and don’t forget to sprinkle in some relevant keywords from the job description!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you’re passionate about fraud prevention and how your skills can help shape our initiatives. Keep it engaging and personal – we want to get to know the real you!
Showcase Your Projects:If you've worked on any relevant projects, make sure to mention them! Whether it's a machine learning model you developed or a dataset you analysed, we love seeing practical examples of your work that relate to fraud detection.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Creditspring
✨Know Your Data Science Tools
Make sure you brush up on your analytical and machine learning toolkit. Be ready to discuss your experience with tools like scikit-learn, pandas, and SQL. Prepare examples of how you've used these tools in past projects, especially in fraud prevention.
✨Showcase Your Collaboration Skills
This role involves working closely with various teams. Think of specific instances where you've collaborated with others, particularly in cross-functional settings. Highlight how you communicated insights and influenced stakeholders to drive change.
✨Prepare for Technical Questions
Expect questions about developing and deploying machine learning models. Brush up on your knowledge of Python and cloud environments. You might be asked to solve a problem on the spot, so practice explaining your thought process clearly.
✨Understand the Business Context
Familiarise yourself with the company's fraud prevention initiatives and the broader lending landscape. Being able to connect your technical skills to business outcomes will show that you understand the importance of your role in driving process and systems change.
