ML Engineer: Statistical Integrity and Label Quality

Wise; Greater London; On-site; GBP 88,000 - 111,000; Full time

Benefits offered by this job

Stock equity (RSUs)

Benefits

Job summary

Wise is seeking an IC3 Machine Learning Engineer in London to improve the label integrity layer for Risk ML models. You will focus on data quality, auditing, and robust statistical methods to ensure reliable model learning from clean data.

You will work with cross-functional partners across Risk Intelligence, Data Engineering, and Product, deploying end-to-end ML pipelines and maintaining high data standards in a fast-moving fintech environment.

Qualifications

• Degree in STEM fields such as CS, math, statistics, physics, chemistry, or related quantitative field.
• Strong mathematical and statistical fundamentals with applied data analysis experience.
• Hands-on ML lifecycle experience across training, evaluation, and deployment (ML/AI/NLP).
• Proficiency in Python or Java for data scripting and production engineering, plus SQL.
• Experience building static data pipelines and using data visualization to understand statistics.

Responsibilities

• Build, scale, and maintain the integrity layer of the label platform for Risk ML models.
• Define, implement, and monitor statistical fundamentals and quality metrics for data and labels.
• Design automated audit processes to evaluate label quality over time.
• Work end-to-end on ML model training, evaluation, and pipeline deployment.
• Collaborate with Risk Intelligence, Data Engineering, and Product teams.

Skills

Statistical analysis

ML lifecycle

Python/Java

SQL

Data visualization

Education

STEM degree

Tools

Kafka

Job description

Wise is seeking an IC3 Machine Learning Engineer in London to improve the label integrity layer for Risk ML models. You will focus on data quality, auditing, and robust statistical methods to ensure reliable model learning from clean data.

You will work with cross-functional partners across Risk Intelligence, Data Engineering, and Product, deploying end-to-end ML pipelines and maintaining high data standards in a fast-moving fintech environment.