Machine Learning Fraud Model Engineer
BMO
Build advanced fraud models with machine learning and deep learning as a hybrid Machine Learning Fraud Model Engineer at BMO. Work from 100 King Street West.Within BMO’s Data Analytics & Reporting group, this role develops data and artificial intelligence products for financial crime management. You will apply algorithms, statistical analysis, forecasting, multivariate testing, and optimization to discover patterns and improve fraud detection. The position also connects technical findings with business needs through clear storytelling and recommendations.Key Responsibilities:
Apply advanced algorithms to create better prediction modelsDiscover patterns across potentially large datasetsResearch practical solutions to financial crime exposuresDevelop data and artificial intelligence productsBuild relationships with internal and external stakeholdersRequirements:
Five to seven years of relevant analytics experienceAdvanced quantitative degree or equivalent experienceExperience with XGBoost, H2O, or SparkML packagesDeep proficiency in quantitative and predictive analyticsFraud modelling experience within a large financial institutionBring advanced algorithms, deep learning, and statistical expertise to BMO’s fraud prediction and financial crime analytics work.
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Apply advanced algorithms to create better prediction modelsDiscover patterns across potentially large datasetsResearch practical solutions to financial crime exposuresDevelop data and artificial intelligence productsBuild relationships with internal and external stakeholdersRequirements:
Five to seven years of relevant analytics experienceAdvanced quantitative degree or equivalent experienceExperience with XGBoost, H2O, or SparkML packagesDeep proficiency in quantitative and predictive analyticsFraud modelling experience within a large financial institutionBring advanced algorithms, deep learning, and statistical expertise to BMO’s fraud prediction and financial crime analytics work.
#J-18808-Ljbffr
Vacancy posted 1 hour ago
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