ML Engineer Statistical Integrity (Financial Crime)

About the role: We are looking for an IC3 Machine Learning Engineer to join our Risk ML and Intelligence 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 its like to work at Wise London!

• Our Engineering career map

• Wise Engineering do we offer:

  • Starting salary:stock equity grants (RSUs vesting over 4 years) benefits.

  • Wise Benefits

  Interested in more than one role If youre interested in multiple roles please apply for just onethe one youre most excited about. If you submit multiple applications requiring the same assessment well continue your recruitment journey using your first application and any duplicate applications will be automatically closed. This helps ensure a fair and consistent interview process. If another role feels like a better fit you can discuss this with your recruiter during the process.

  #LI-AB3 #LI-Hybrid

  For everyone everywhere. Were people building money without borders  without judgement or prejudice too. We believe teams are strongest when they are diverse equitable and inclusive.

  Were 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 its like to work at Wise visit .

  Keep up to date with life at Wise by following us on LinkedIn and Instagram.

  
  

  Remote Work :

  No

  
  

  Employment Type :

  Full-time