Data Science Lead - AML Risk

Data Science Lead - AML Risk

Full-Time 85000 - 115000 £ / year (est.) No working from home possible

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 career 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 collaborative mindset.

The predicted salary is between 85000 - 115000 £ 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. This is an exciting opportunity to develop the program in a global company. Your work will allow Wise to keep our customers safe and ensure we can 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 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:

  • 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. So 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.

Additional Information: For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive. 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: 慨正橡扯

Wise is an exceptional employer, offering a dynamic work culture that prioritises innovation and inclusivity. As a Data Science Lead in London, you will have the opportunity to shape cutting-edge AML detection systems while collaborating with a diverse team dedicated to making a global impact. With a strong focus on employee growth and development, Wise empowers its staff to thrive in their careers, ensuring that every voice is heard and valued.

Contact Details:

慨正橡扯 Recruitment 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 people in the industry, especially those already at Wise. A friendly chat can open doors and give you insider info on what they're really looking for.

Tip Number 2

Show off your skills! Prepare a portfolio or case studies that highlight your experience with machine learning and AML systems. This will help you stand out during interviews and showcase your expertise.

Tip Number 3

Practice makes perfect! Get ready for technical interviews by brushing up on your Python skills and understanding of machine learning concepts. Mock interviews can help you feel more confident.

Tip Number 4

Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the Wise team and contributing to our mission.

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 CV:Make sure your CV is tailored to the Data Science Lead role. Highlight your experience with machine learning, Python, and any relevant projects that showcase your skills in AML risk. We want to see how you can contribute to our mission!

Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about the role and how your background aligns with our goals at Wise. Be genuine and let your personality come through – we love authenticity!

Showcase Your Problem-Solving Skills:In your application, don’t just list your skills; demonstrate them! Share specific examples of how you've tackled complex problems in the past, especially in the context of machine learning or financial crime. We’re keen to see your thought process!

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at Wise!

How to prepare for a job interview at 慨正橡扯

Know Your 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 have practical knowledge too.

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 data science or AML risk and how you approached them. This will demonstrate your analytical thinking and ability to refine problem statements.

Communicate Clearly

Since you'll be working with non-technical stakeholders, practice explaining your technical work in simple terms. Prepare to articulate your thought process and findings clearly, so everyone understands the value of your contributions. Good communication can set you apart!

Cultural Fit Matters

Wise values diversity and inclusion, so be prepared to discuss how your background and experiences align with their mission. Show enthusiasm for their goals and how you can contribute to a diverse team. This will help you connect with the interviewers on a personal level.