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: Dynamic role with opportunities to mentor and build a high-performing team.
- Why this job: Make a real impact on global financial safety while working with cutting-edge technology.
- Qualifications: Experience in machine learning, strong Python skills, and a collaborative mindset.
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 employer: Dangote Industries Limited
Wise is an exceptional employer, offering a dynamic work culture in the heart of London where innovation meets purpose. As a Data Science Lead in the AML Risk team, you will not only have the opportunity to shape cutting-edge machine learning solutions but also enjoy a supportive environment that fosters professional growth and collaboration. With a commitment to employee development and a mission-driven approach, Wise empowers its team members to make a meaningful impact on the safety of millions of customers worldwide.
Contact Details:
Dangote Industries Limited Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Science Lead - AML Risk
✨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 AML or data science. Building relationships can lead to job opportunities that aren’t even advertised.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects, especially those related to machine learning and AML. Share it on platforms like GitHub or your personal website to give potential employers a taste of your expertise.
✨Ace the Interview
Prepare for interviews by practising common questions and scenarios related to data science and AML. Use the STAR method (Situation, Task, Action, Result) to structure your answers and demonstrate your problem-solving skills effectively.
✨Apply Through Our Website
Make sure to apply directly through our website! It’s the best way to ensure your application gets seen by the right people. Plus, you’ll be one step closer to joining our amazing team at Wise!
We think you need these skills to ace Data Science Lead - AML Risk
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Science Lead role. Highlight your experience with machine learning systems and any relevant projects you've worked on, especially those related to AML or financial crime.
Showcase Your Skills:In your application, don’t just list your skills—show us how you’ve used them! Provide examples of how you've implemented and evaluated machine learning systems, particularly in a team setting.
Be Clear and Concise:When writing your cover letter, keep it clear and to the point. We want to see your passion for the role and how you can contribute to our mission, but avoid fluff—get straight to the good stuff!
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 track your application and ensure it reaches the right people!
How to prepare for a job interview at Dangote Industries Limited
✨Know Your Machine Learning Inside Out
Make sure you brush up on your knowledge of unsupervised, semi-supervised, and supervised learning techniques. Be ready to discuss how you've implemented these in past projects, especially in relation to AML detection systems.
✨Showcase Your Python Skills
Since strong Python knowledge is crucial for this role, prepare to demonstrate your coding skills. You might be asked to solve a problem on the spot, so practice writing clean, efficient code that adheres to OOP principles.
✨Communicate Clearly with Non-Technical Folks
You’ll need to explain complex concepts to non-technical team members. Practice simplifying your explanations and using relatable analogies to ensure everyone understands your ideas and solutions.
✨Prepare for Problem-Solving Scenarios
Expect to tackle real-world problems during the interview. Think about past challenges you've faced in your work and how you approached them. Be ready to discuss your thought process and the outcomes of your solutions.
