ml engineer in fintech

Описаниеwise
London, UK
15 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Java (Programming Language) Artificial Intelligence Artificial Neural Networks Information Engineering Data Visualization Python (Programming Language) Machine Learning Natural Language Processing Support Vector Machine Kaggle Information Technology Static Data
+1 more
Data Pipelines

Requirements

ОписаниеWise 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.ЗадачиBuild, scale, and maintain the integrity layer of the label platform for Risk ML modelsDefine, implement, and monitor statistical fundamentals and key quality metrics for data and labelsDesign automated audit processes to evaluate and monitor label quality over timeWork end-to-end on machine learning model training, evaluation, and pipeline deploymentCollaborate closely with cross-functional partners across Risk Intelligence, Data Engineering, and ProductТребованияHold a degree in STEM, such as Computer Science, Mathematics, Statistics, Physics, Chemistry, Electrical Engineering, or a related quantitative fieldApply strong mathematical and statistical fundamentals to complex data environmentsHave hands-on experience across model training, evaluation, and deployment using Machine Learning, AI, Neural Networks, or NLP frameworksDemonstrate strong proficiency in Python or Java for data scripting and production engineeringHave advanced SQL skillsBuild static data pipelines and conduct deep-dive data analysisUse data visualization tools to understand statistical behaviorNice to have: success in competitive machine learning environments or platforms such as Kaggle, KDD competitions, or Google Summer of Code / GSoCexperience with Graph Neural Networks (GNNs), Support Vector Machines (SVM), Natural Language Processing (NLP), Transformers, or LSTMsfamiliarity with real-time streaming data pipelines such as Kafkadomain experience in Fintech, E-commerce, or fast-scaling tech companiesУсловияNo conditions specified #J-18808-Ljbffr

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