ml engineer in fintech

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

• Hold a degree in STEM, such as Computer Science, Mathematics, Statistics, Physics, Chemistry, Electrical Engineering, or a related quantitative field
• Apply strong mathematical and statistical fundamentals to complex data environments
• Have hands-on experience across model training, evaluation, and deployment using Machine Learning, AI, Neural Networks, or NLP frameworks
• Demonstrate strong proficiency in Python or Java for data scripting and production engineering
• Have advanced SQL skills
• Build static data pipelines and conduct deep-dive data analysis
• Use data visualization tools to understand statistical behavior
• Nice to have: success in competitive machine learning environments or platforms such as Kaggle, KDD competitions, or Google Summer of Code / GSoC
• experience with Graph Neural Networks (GNNs), Support Vector Machines (SVM), Natural Language Processing (NLP), Transformers, or LSTMs
• familiarity with real-time streaming data pipelines such as Kafka
• domain experience in Fintech, E-commerce, or fast-scaling tech companies

Wise is a global technology company building services for moving and managing money worldwide. It enables people and businesses to send money internationally, spend abroad, and make and receive international payments.