Wise
Data Science Lead - AML Risk
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
On the Role — You’ll lead our AML Risk team in London as the Data Science Lead, overseeing a growing Data Science team that builds machine learning-based technical solutions for AML detection across all Wise licences. Your work will underpin Wise’s global mission—ensuring customer safety, combating financial crime, and scaling systems for millions of users worldwide.
About the Role: The AML Risk team is shaping a high-impact network that integrates unsupervised, semi-supervised, supervised learning, and GenAI to detect financial crime globally. You’ll build cutting-edge systems, guide a skilled team, and balance technical execution with strategic vision.
Your contributions will span:
1. AML Risk Detection System Development
- Design and implement AML detection controls using:
- Unsupervised/semi-supervised/supervised machine learning
- Generative AI for threat detection
- Develop regional coverage frameworks to validate system effectiveness
- Tailor solutions for Wise’s global, diverse user base
2. Team Growth & Leadership
- Hire and mentor specialists at all levels
- Collaborate with product managers and engineering leads to assess hiring needs
- Foster a culture of high performance by mentoring in technical and soft skills
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
3. Performance Testing & Optimisation
- Benchmark AML systems against internal/external standards
- Optimise precision/recall trade-offs via data-driven decisioning
- Run scenarios to analyse system impacts under varied conditions
4. Operational Process Development
- Partner with operations to improve feedback loops into automation
- Create projects leveraging operational capacity (e.g., manual data labelling for models)
- Develop investigator tools highlighting red flags and transactional patterns
5. Deployment & Implementation
- Package algorithms into deployable formats (libraries/objects)
- Transition models from staging to production environments
- Maintain production-grade Python services
- Implement automated pipelines for data gathering and retraining
Requirements / Skills
- Proven experience in implementing, training, evaluating ML systems
- Python expertise—preferably with OOP principles
- Strong statistical analysis skills and experiment design
- Product-minded ability to work independently in cross-functional/cross-team settings
- Sharp communications skills adept at explaining technical concepts to non-technical stakeholders
- Problem-solving acumen to refine problems and solutions
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Bonus (Non-Essential) Skills
- Automation experience using Large Language Models for operational tasks
- Willingness to immerse in operational workflows side-by-side
- Background in financial crime detection/Digital Forensics
Company Ethos Wise champions diversity and inclusion—your background matters less than your skills, collaboration, and passion for adaptable problem-solving. Empowered teams produce exceptional products, and we prioritise candidates from under-represented groups.
Earnings: Base range: £90,500–£127,000/year Learn more about Wise cultures or connect on LinkedIn/Instagram for insights into team life.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location