At a Glance
- Tasks: Lead the Data Science team to develop AML detection systems using advanced machine learning techniques.
- Company: Join Wise, a forward-thinking company dedicated to keeping customers safe from financial crime.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on mentorship and career development.
- Why this job: Make a real impact on global financial safety while leading innovative tech solutions.
- Qualifications: Experience in machine learning, strong Python skills, and a knack for problem-solving.
The predicted salary is between 80000 - 100000 £ per year.
We’re looking for a Data Science Lead to join our AML Risk team in London. This role is a unique opportunity to build out the lead Data Science team and machine learning‑based technical solutions in the AML Risk team, which owns AML detection across all of the Wise licenses. Your work will allow Wise to keep our customers safe and keep our ecosystem free of bad actors in a scalable way. What you build will have a direct impact on Wise’s mission and millions of our customers.
About the Role
In the Anti‑Money Laundering (AML) Risk team we are developing systems that mix unsupervised, supervised learning and GenAI to detect and mitigate Financial Crime on a global scale. You will ensure the AML Risk Data Science team is well‑equipped and working on cutting‑edge technology to sustainably support Wise’s growing customer, transaction and product space.
Here’s how you’ll be contributing:
- AML Risk Detection System Development
- Developing efficient and effective AML detection controls using a mixture of unsupervised, semi‑supervised and supervised learning with GenAI
- Creating frameworks to prove controls coverage at a regional level
- Developing technologies to serve Wise’s diverse international user base
- Building a team of high performing specialists
- Working with product managers and engineering leads to understand staffing requirements
- Hiring specialists
- Mentoring more junior members of the team on technical and non‑technical skillsets
- Performance Testing and Optimisation
- Evaluating our AML systems against internal and external benchmarks
- Developing decisioning layers to find optimal trade‑offs between precision and recall
- Providing data‑driven insights on potential outcomes under various scenarios
- Operational Process Development
- Collaborating with operational teams to refine processes, ensuring effective feedback integration into automation systems
- Designing and managing projects that utilise excess operational capacity, such as manual data labelling for model improvement
- Creating systems which provide in‑depth insight to investigators on red flags and typologies present on profiles/transactions
- Deployment and Implementation
- Packaging algorithms into deployable libraries/objects and transitioning them from staging to production environments
- Implementing and maintaining scheduled processes for data gathering and model retraining using automated pipelines
- Maintaining production‑grade Python services
A bit about you:
- Experience implementing, training, testing and evaluating performance of Machine Learning systems
- Strong Python knowledge; experience with OOP principles is a plus
- Experience with statistical analysis and the ability to produce well‑designed experiments
- A strong product mindset with the ability to work independently in a cross‑functional and cross‑team environment
- Good communication skills and ability to get the point across to non‑technical individuals
- Strong problem‑solving skills with the ability to help refine problem statements and figure out how to solve them
Some extra skills that are great (but not essential):
- Familiarity with automating operational processes via technical solutions, e.g., Large Language Models
- Willingness to get hands dirty with operational side by side to understand pain points
- Knowledge and experience within the Financial Crime domain
Data Science Lead - AML Risk in London employer: Dangote Industries Limited
Wise is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration within the AML Risk team in London. Employees benefit from opportunities for professional growth, mentorship, and the chance to work on cutting-edge technology that directly impacts the safety of millions of customers. With a commitment to employee well-being and a focus on meaningful contributions, Wise stands out as a rewarding place to advance your career in data science.
Contact Details:
Dangote Industries Limited Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Lead - AML Risk in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at Wise. A friendly chat can sometimes lead to opportunities that aren’t even advertised!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to machine learning and AML. This gives you a chance to demonstrate your expertise and passion for the field.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with non-technical folks.
✨Tip Number 4
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 genuinely interested in joining the Wise team.
We think you need these skills to ace Data Science Lead - AML Risk in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Data Science Lead in AML Risk. Highlight your experience with machine learning systems and any relevant projects that showcase your skills in financial crime detection.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about AML and how your background makes you a perfect fit for our team. Share specific examples of your work that align with the responsibilities outlined in the job description.
Showcase Your Technical Skills:Don’t forget to mention your Python expertise and any experience with OOP principles. We want to see how you've implemented machine learning solutions in the past, so be specific about your contributions and outcomes.
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 ensure it gets the attention it deserves!
How to prepare for a job interview at Dangote Industries Limited
✨Know Your Data Science Stuff
Make sure you brush up on your machine learning concepts, especially unsupervised and supervised learning. Be ready to discuss how you've implemented these in past projects, as well as any experience with GenAI. The more specific examples you can provide, the better!
✨Show Off Your Team-Building Skills
Since this role involves building a high-performing team, be prepared to talk about your experience in hiring and mentoring. Share stories that highlight your leadership style and how you've helped junior members grow their skills.
✨Communicate Clearly
You’ll need to explain complex technical concepts to non-technical folks, so practice simplifying your language. Think of ways to convey your ideas clearly and concisely, perhaps by using analogies or straightforward examples from your work.
✨Be Ready for Problem-Solving Scenarios
Expect some situational questions where you'll need to demonstrate your problem-solving skills. Prepare to discuss how you've tackled challenges in previous roles, particularly those related to AML systems or financial crime detection.
