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описание
Wise is a global technology company building a way to move and manage money worldwide. It enables people and businesses to send money internationally, spend abroad, and make international payments.
задачи
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 with cross-functional partners across Risk Intelligence, Data Engineering, and Product.
требования
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;
Have strong proficiency in Python or Java for data scripting and production engineering;
Have advanced SQL skills;
Have hands-on experience building static data pipelines;
Conduct deep-dive data analysis and 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/LSTMs, familiarity with real-time streaming data pipelines such as Kafka, domain experience in Fintech, E-commerce, or fast-scaling tech companies.
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