Lead AI Data Scientist - Servicing in London
Lead AI Data Scientist - Servicing

Lead AI Data Scientist - Servicing in London

London Full-Time 73000 - 97000 £ / year (est.) Home office (partial)
Wise

At a Glance

  • Tasks: Lead AI projects to automate financial crime mitigation and enhance operational workflows.
  • Company: Wise, a global tech company revolutionising money management.
  • Benefits: Competitive salary, inclusive culture, and opportunities for career growth.
  • Why this job: Make a real impact with cutting-edge AI solutions in a dynamic environment.
  • Qualifications: Experience in machine learning, strong Python skills, and a product-focused mindset.
  • Other info: Join a diverse team committed to innovation and inclusivity.

The predicted salary is between 73000 - 97000 £ per year.

Compensation: GBP 85,000 - GBP 115,000 - yearly

Company Description

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.

The Servicing team offers an exciting environment for applying cutting-edge Generative AI solutions. This team is dedicated to enhancing our financial crime mitigation operations through tooling and automation, aiming to streamline reviews and simplify the work of our operations staff. A core focus involves analyzing and automating significant parts of our operational workflows. Furthermore, the team develops LLM-based tools to assist crime prevention teams in deflecting demand. The successful candidate will have the chance to directly contribute to Wise's mission by tackling these challenges and developing a comprehensive testing suite for our solutions.

Here’s how you’ll be contributing:

  • End-to-End Automation: Lead the development and deployment of AI models designed to augment operational workflows, specifically targeting the automation of case comments, red flag generation, final review summaries, and data labeling.
  • Full-Stack Deployment: Take ownership of the production pipeline by writing and deploying production-ready Python services. You must be willing to bypass engineering bottlenecks to ship value quickly while maintaining code quality.
  • Human-in-the-Loop Architecture: Design systems where AI provides recommendations and drafts, ensuring human operators retain the final decision-making authority for critical financial crime mitigation assessments.
  • Rigorous Testing & Governance: Establish comprehensive testing frameworks (e.g., shadow mode, A/B testing) for production environments and act as the technical liaison with Compliance to ensure all models meet regulatory standards prior to launch.
  • Strategic Demand Deflection: Go beyond ticket handling by analyzing upstream data to create strategies that deflect financial crime attempts before they reach the operations team, effectively reducing manual workload.
  • Mentorship & Leadership: Lead and grow other Junior Data Scientists, fostering a product-focused mindset and guiding them through complex technical implementations and architectural decisions.

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;
  • Knowledge and experience developing Unsupervised Learning methods;
  • 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):

  • Hands-on experience training Neural Network models and deploying them into production
  • Familiarity with automating operational processes via technical solutions, for example Large Language Models
  • Experience implementing fine-tuning, reinforced learning alignment and evaluation techniques within an LLM training pipeline.
  • Familiarity with agentic frameworks such as LangGraph or similar.
  • Willingness to get hands dirty reading many, many historical operational cases.

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. If you want to find out more about what it's like to work at Wise visit Wise.Jobs.

Lead AI Data Scientist - Servicing in London employer: Wise

Wise is an exceptional employer that fosters a dynamic and inclusive work culture, where innovation meets purpose. As a Lead AI Data Scientist in the Servicing team, you will have the opportunity to work with cutting-edge Generative AI solutions while contributing directly to our mission of simplifying financial transactions for everyone, everywhere. With a strong emphasis on mentorship, employee growth, and a commitment to diversity, Wise provides a supportive environment that empowers you to thrive and make a meaningful impact in the world of finance.
Wise

Contact Detail:

Wise Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Lead AI Data Scientist - Servicing in London

✨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 Servicing team. Personal connections can make a huge difference!

✨Tip Number 2

Prepare for the interview by diving deep into Wise's mission and values. Understand how your skills in AI and data science can directly contribute to their goals, especially in financial crime mitigation. Show them you’re not just another candidate, but someone who truly gets what they’re about.

✨Tip Number 3

Practice your problem-solving skills! Be ready to tackle real-world scenarios during interviews. Think about how you would approach automating workflows or developing AI models for financial crime prevention. The more you can demonstrate your thought process, the better!

✨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. Let’s get you that dream job!

We think you need these skills to ace Lead AI Data Scientist - Servicing in London

Machine Learning
Python
Object-Oriented Programming (OOP)
Unsupervised Learning
Statistical Analysis
Experimental Design
Communication Skills
Problem-Solving Skills
AI Model Development
End-to-End Automation
Full-Stack Deployment
Human-in-the-Loop Architecture
Testing Frameworks
Financial Crime Mitigation
Mentorship and Leadership

Some tips for your application 🫡

Show Your Passion: When writing your application, let your enthusiasm for AI and financial crime mitigation shine through. We want to see how your passion aligns with our mission at Wise!

Tailor Your Experience: Make sure to highlight your relevant experience in machine learning and Python. We’re looking for specific examples that demonstrate your skills and how they can contribute to our Servicing team.

Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to explain your achievements and how you’ve tackled challenges in the past. We appreciate clarity!

Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and get the ball rolling on your journey with Wise.

How to prepare for a job interview at Wise

✨Know Your AI Stuff

Make sure you brush up on your knowledge of machine learning systems and Python. Be ready to discuss your experience with unsupervised learning methods and how you've implemented them in past projects. This role is all about cutting-edge AI solutions, so showing your expertise will definitely impress.

✨Showcase Your Problem-Solving Skills

Prepare to talk about specific challenges you've faced in previous roles and how you tackled them. Wise is looking for someone who can refine problem statements and come up with effective solutions, so having concrete examples will help demonstrate your capabilities.

✨Communicate Clearly

Since you'll be working with cross-functional teams, it's crucial to articulate your thoughts clearly, especially to non-technical individuals. Practice explaining complex concepts in simple terms, as this will show that you can bridge the gap between technical and non-technical stakeholders.

✨Emphasise Team Leadership

If you've had experience mentoring or leading junior data scientists, make sure to highlight that. Wise values a product-focused mindset and leadership skills, so sharing your approach to guiding others through complex technical implementations will set you apart from other candidates.

Lead AI Data Scientist - Servicing in London
Wise
Location: London

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