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- Tasks: Design and deploy machine learning models to detect fraud in payment flows.
- Company: Exciting B2B FinTech startup focused on fraud prevention.
- Benefits: Up to £135k salary, generous equity, and flexible remote work.
- Why this job: Join a fast-growing team and shape the future of digital payments.
- Qualifications: Experience in fraud detection systems and strong programming skills in Python.
- Other info: Opportunity to work with Tier 1 banks and make a real impact.
The predicted salary is between 120000 - 135000 £ per year.
This London startup is building a new intelligence layer designed to bring more context and security to digital payments. Their technology analyses transactions in real time, gathering signals from multiple sources to determine whether a payment is legitimate or potentially fraudulent. The platform combines distributed data systems, real-time investigations and AI-driven decisioning to help financial institutions detect scams while allowing legitimate payments to flow without unnecessary friction. Within 2 years of being founded, they are working with most Tier 1 banks and payment providers in the UK.
They are now looking for an experienced Data Scientist (AI/ML Engineer) with deep fraud or financial crime experience (ideally APP fraud exposure) to join at an early stage and help shape the core intelligence powering the platform.
Key responsibilities:
- Designing and deploying machine learning models used to detect fraud and financial crime in payment flows
- Building features from heterogeneous data sources, including transaction data, contextual signals and unstructured information
- Improving systems that extract useful signals from fragmented or unstructured data sources
- Building reliable ML infrastructure to train, deploy and monitor models in production environments
- Working closely with product and engineering teams to ensure models improve real-world fraud outcomes
- Identifying the fraud signals, typologies and data sources that meaningfully improve detection capability
- Experimenting with both classical ML techniques and newer AI approaches where appropriate
- Helping shape data strategy, including how feedback loops and labelling pipelines are built to improve models over time
This role is focused on shipping production systems rather than academic research.
Must have requirements:
- Strong practical experience building fraud detection systems or financial crime models in production
- Deep FinCrime / FinTech / Payments domain expertise
- Product mindset - focus on improving real-world outcomes, not just model metrics
- Experience working in fast-moving environments where systems are built from scratch and priorities evolve quickly
- Experience working with heterogeneous datasets (transaction data, enrichment signals, text, network signals etc.)
- Familiarity with model monitoring, drift detection and retraining pipelines
- Strong SQL and data engineering capability
- Strong programming skills in Python
Bonus points for:
- Exposure to / understanding of APP Fraud, payment fraud or transaction monitoring
- Previous experience working in an early stage start-up and/or high growth scale up
- Exposure to newer approaches such as LLM-powered systems
- Cloud infrastructure / data platforms experience, ideally GCP
VISA sponsorship is available if needed.
Senior Data Scientist (FinCrime / Fraud) in City of London employer: Wave Group
Contact Detail:
Wave Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist (FinCrime / Fraud) in City of London
✨Tip Number 1
Network like a pro! Reach out to people in the FinTech and fraud prevention space on LinkedIn. Join relevant groups, attend meetups, and don’t be shy about asking for informational interviews. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning models and fraud detection systems. Use GitHub or a personal website to display your projects. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Research the company’s tech stack and be ready to discuss how your experience aligns with their needs. Brush up on your SQL and Python skills, and think of examples where you’ve improved real-world outcomes in previous roles.
✨Tip Number 4
Apply through our website! We’re always on the lookout for talented individuals like you. Make sure to tailor your application to highlight your FinCrime expertise and product mindset. Let’s get you on board to help shape the future of fraud prevention!
We think you need these skills to ace Senior Data Scientist (FinCrime / Fraud) in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Data Scientist role. Highlight your experience in fraud detection systems and financial crime models, as well as any relevant projects you've worked on. We want to see how your skills align with our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for FinTech and how you can contribute to our team. Be specific about your experience with machine learning models and how they’ve improved real-world outcomes in your previous roles.
Showcase Your Technical Skills: Don’t forget to highlight your technical skills, especially in Python and SQL. Mention any experience with cloud infrastructure or data platforms, as these are key to our operations. We love seeing practical examples of your work!
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right people. We can’t wait to hear from you!
How to prepare for a job interview at Wave Group
✨Know Your Stuff
Make sure you brush up on your knowledge of fraud detection systems and financial crime models. Be ready to discuss your practical experience in building these systems, especially in production environments. The more specific examples you can provide, the better!
✨Showcase Your Product Mindset
This role is all about improving real-world outcomes, so be prepared to talk about how your work has positively impacted previous projects. Think about times when your models made a difference in detecting fraud or enhancing payment security.
✨Get Familiar with Their Tech Stack
Research the technologies and methodologies they use, especially around machine learning and data engineering. If you have experience with SQL, Python, or cloud platforms like GCP, make sure to highlight that during the interview.
✨Ask Insightful Questions
Prepare some thoughtful questions about their current challenges in fraud detection and how they envision the role contributing to their goals. This shows your genuine interest in the position and helps you understand if it’s the right fit for you.