Senior Data Scientist, Fraud Applied AI and Innovation

Job Description

Job Description What's the opportunity? You will apply machine learning, artificial intelligence and advanced analytical methodologies to support Fraud Management’s key priorities, including developing predictive models for improving fraud detection capabilities, optimizing existing productivity tools, creating automated workflows to replace manual processes, designing forward thinking and innovative solutions to complex problems, etc. You will represent the Applied AI & Innovation team as a Subject Matter Expert (SME) on projects and initiatives across Fraud Management (FM) and collaborate with multiple stakeholders at varying levels of seniority. You will also assist in developing best practices for analytical processes and training junior team members. What will you do? Develop machine learning (ML) models for real-time fraud detection and explore opportunities for applying ML solutions to other challenges within FM, while following all the standards set for model development Contribute to developing ML strategy for FM, collaborate with peers to integrate the model into the detection ecosystem and monitor, evaluate and optimize tools and technologies developed to improve FM efficiency Collaborate with Detection Analytics partners to incorporate feedback and ensure any changes resulting in an impact to the detection ecosystem are communicated Identify opportunities and develop automated pipelines to replace or enhance existing processes, utilizing the full suite of available technology and tools to build the most effective solution Provide thought leadership to support FM’s key priorities where there is a dependence on data analytics or machine learning and deliver on all initiatives that support strategic imperatives in line with project timelines Develop predictive data models, quantitative analyses and visualization of targeted, big data sources and oversee acquisition of data while ensuring data quality and comprehensiveness Lead data exploration and analytic projects, provide on-going coaching of big data topics and explore semantic data capabilities through NLP, text mining and machine learning techniques What you need to succeed Must have: 2 years’ experience in Machine Learning, data mining and statistics Ability to perform complex data analysis on large volumes of data and present findings to stakeholders Knowledge of Canadian banking and payment industry, payments transaction data and financial fraud Strong practical knowledge of, and proven experience with, analytical software packages and various programming languages (Python, SQL, etc.) Working knowledge of Big Data Framework (Hadoop, Spark, etc.) Exceptional communication and presentation skills, including ability to effectively communicate analytical recommendations to both technical and non-technical audiences Excellent time management skills and demonstrated ability to prioritize workload and manage multiple projects simultaneously Degree in a quantitative discipline and strong

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