Data Science Lead - AML Risk

Data Science Lead - AML Risk

Full-Time 80000 - 100000 € / year (est.) No home office possible
hackajob

At a Glance

  • Tasks: Lead the Data Science team to develop AML detection systems using cutting-edge machine learning.
  • Company: Join Wise, a global tech company revolutionising money management.
  • Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
  • Other info: Diverse and inclusive team environment with a focus on personal development.
  • Why this job: Make a real impact on financial safety for millions of customers worldwide.
  • Qualifications: Experience in machine learning, strong Python skills, and a problem-solving mindset.

The predicted salary is between 80000 - 100000 € per year.

Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money. For everyone, everywhere.

We’re looking for a Data Science Lead to join our AML Risk team in London. This role is a unique opportunity to work on building 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 to 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.

In the Anti-Money Laundering (AML) Risk team we are developing systems which are a mixture of unsupervised and supervised learning, with GenAI to detect and mitigate Financial Crime on a global scale. You will be ensuring 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. You will be stepping into an already functioning, but growing product team.

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 our 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. A big plus for proven familiarity and experience with OOP principles.
  • Experience with statistical analysis, and 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, for example Large Language Models.
  • Willingness to get hands dirty with operational side by sides to understand their pain points.
  • Knowledge and experience within the Financial Crime domain.

We’re people without borders — without judgement or prejudice, too. We want to work with the best people, no matter their background. If you’re passionate about learning new things and keen to join our mission, you’ll fit right in. Also, qualifications aren’t that important to us. If you’ve got great experience, and you’re great at articulating your thinking, we’d like to hear from you. And because we believe that diverse teams build better products, we’d especially love to hear from you if you’re from an under‑represented demographic. We’re proud to have a truly international team, and we celebrate our differences. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.

Data Science Lead - AML Risk employer: hackajob

Wise is an exceptional employer, offering a dynamic work environment in London where innovation meets purpose. As a Data Science Lead in the AML Risk team, you will not only contribute to cutting-edge technology that safeguards millions of customers but also benefit from a culture that values diversity, continuous learning, and professional growth. With opportunities to mentor and lead a high-performing team, Wise empowers its employees to make a meaningful impact while enjoying a supportive and inclusive workplace.

hackajob

Contact Detail:

hackajob Recruiting Team

StudySmarter Expert Advice🤫

We think this is how you could land Data Science Lead - AML Risk

Tip Number 1

Network like a pro! Reach out to current employees at Wise on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing a role in the AML Risk team. Personal connections can make a huge difference!

Tip Number 2

Prepare for the interview by brushing up on your machine learning knowledge and Python skills. Be ready to discuss your past projects and how they relate to AML detection. Show us your problem-solving skills and how you can contribute to our mission!

Tip Number 3

Don’t just focus on technical skills; highlight your ability to communicate complex ideas clearly. We want to see how you can bridge the gap between tech and non-tech teams. Practice explaining your work in simple terms!

Tip Number 4

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 our team at Wise. Let’s make money management easier together!

We think you need these skills to ace Data Science Lead - AML Risk

Machine Learning
Python
Statistical Analysis
Unsupervised Learning
Supervised Learning
GenAI
Performance Testing

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter for the Data Science Lead role. Highlight your experience with machine learning systems and any relevant projects that align with AML risk detection. We want to see how your skills fit into our mission!

Showcase Your Problem-Solving Skills:In your application, don’t just list your skills—give us examples of how you've tackled complex problems in the past. We love seeing candidates who can think critically and come up with innovative solutions, especially in the financial crime domain.

Be Clear and Concise:When writing your application, keep it straightforward. Use clear language and avoid jargon where possible. We appreciate candidates who can communicate effectively, especially when explaining technical concepts to non-technical folks.

Apply Through Our Website:We encourage you to submit your application directly through our website. It’s the best way to ensure it gets to the right people. Plus, you’ll find all the details about the role and our company culture there!

How to prepare for a job interview at hackajob

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. This will show that you're not just familiar with the theory but can apply it practically.

Showcase Your Problem-Solving Skills

Prepare examples of how you've tackled complex problems in your previous roles. Think about specific challenges you've faced in AML or financial crime detection and how you approached them. This will demonstrate your analytical mindset and ability to refine problem statements.

Communicate Clearly

Since you'll be working with cross-functional teams, practice explaining technical concepts in simple terms. Prepare to share how you've successfully communicated with non-technical stakeholders in the past. This will highlight your ability to bridge the gap between tech and business.

Be Ready to Discuss Team Dynamics

As a lead, you'll be building and mentoring a team. Think about your leadership style and how you've supported junior members in their development. Be prepared to discuss how you would approach hiring and fostering a high-performing team within the AML Risk context.