At a Glance
- Tasks: Build and maintain machine learning models for financial crime detection.
- Company: Join Wise, a global tech company revolutionising money management.
- Benefits: Competitive salary, stock options, and flexible working arrangements.
- Other info: Diverse and inclusive team culture with excellent career growth opportunities.
- Why this job: Make a real impact in the fight against financial crime with cutting-edge technology.
- Qualifications: Degree in STEM and strong skills in Python or Java required.
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.
Job Description
About the role: We are looking for an IC3 Machine Learning Engineer to join our Risk ML and Intelligence team. In this role, you will be key to enabling the building of our machine learning models by focusing on the label side, building the integrity layer for our label platform. Every machine learning model at Wise learns from two core components: features (user signals) and labels (historical tags for activity like money laundering or fraud). If our labels are inaccurate, our models learn the wrong behavior. You will be responsible for label side quality, label monitoring, statistical integrity, and designing robust audit processes to ensure our ML infrastructure learns from clean, reliable data.
How we work: At Wise, we operate with autonomous, cross-functional teams that put the customer first. We believe strong engineers can learn and adapt across tech stacks, so our interview and pair programming evaluations are language-agnostic (focused on Python or Java), allowing you to solve complex technical problems in the environment you are most comfortable with.
What will you be working on?
- Building, scaling, and maintaining the integrity layer of our label platform for Risk ML models.
- Defining, implementing, and monitoring statistical fundamentals and key quality metrics for data and labels.
- Designing automated audit processes to evaluate and monitor label quality over time.
- Working end-to-end on machine learning model training, evaluation, and pipeline deployment.
- Collaborating closely with cross-functional partners across Risk Intelligence, Data Engineering, and Product.
Qualifications
What do you need?
- Education: A degree in STEM (Computer Science, Mathematics, Statistics, Physics, Chemistry, Electrical Engineering, or a related quantitative field).
- Statistical Integrity: Strong mathematical and statistical fundamentals with a proven track record of applying statistical analysis to complex data environments.
- ML Lifecycle Expertise: Hands-on experience working across model training, evaluation, and deployment (utilizing frameworks around Machine Learning, AI, Neural Networks, or NLP).
- Programming Skills: Strong proficiency in Python or Java for data scripting and production engineering, alongside advanced SQL capability.
- Data Fundamentals: Solid hands-on experience building static data pipelines, conducting deep-dive data analysis, and using data visualization tools to understand statistical behavior.
Nice to Have
- Proven success in competitive machine learning environments or platforms (e.g., Kaggle, KDD competitions, or Google Summer of Code / GSoC).
- Experience with specialized ML architectures such as Graph Neural Networks (GNNs), Support Vector Machines (SVM), Natural Language Processing (NLP), or Transformers/LSTMs.
- Familiarity with real-time streaming data pipelines (e.g., Kafka).
- Domain experience within Fintech, E-commerce, or fast-scaling tech companies.
Additional Information
Interested? Find out more: How we work – a practical guide DEI @ Wise Wise Tech Stack (2025 update) See what it's like to work at Wise London! Our Engineering career map Wise Engineering – https://medium.com/wise-engineering
What do we offer?
Starting salary: £87,500 - £111,000 + stock equity grants (RSUs vesting over 4 years) + benefits.
Wise Benefits
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. Keep up to date with life at Wise by following us on LinkedIn and Instagram.
ML Engineer - Statistical Integrity (Financial Crime) employer: Wise group
Wise is an exceptional employer that fosters a collaborative and innovative work culture in the heart of London. With a strong emphasis on employee growth, you will have the opportunity to lead a talented team while developing your skills in a dynamic environment. The company offers competitive benefits and a commitment to work-life balance, making it a rewarding place for those looking to make a meaningful impact in the KYC verification space.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineer - Statistical Integrity (Financial Crime)
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Wise group!
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Wise group.
✨Apply Directly through Our Website
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We think you need these skills to ace ML Engineer - Statistical Integrity (Financial Crime)
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Wise group, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Wise group. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Wise group
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Wise group!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
