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- Collaborate with stakeholders including Operations, Analytics and Data Science partners, as well as Payments, Scotia Digital, and IT&S partners to identify improvement opportunities and drive incremental business value in reducing fraud risks
- Leverage distributed computing tools Beam, BigQuery, Hive, Spark) for analysis, data mining and modeling
- Collaborate with technology / engineering, analytics, and operational teams to deploy fraud detection and prevention models and algorithms in production across different products and channels
- Create and apply model validation strategies to measure model effectiveness, conduct A/B testing, and deliver ongoing enhancements
- Prepare detailed documentation to outline data sources, models and algorithms used and developed
- Present results to business line stakeholders and help implement data-driven decisioning, automation and associated operational change management with real business impact
- Champion a customer focused culture to deepen client relationship and leverage broader inter-departmental relationships, as well as knowledge of systems and processes
- Leverage Scrum framework for iterative and incremental agile development of machine learning product features, participate in sprint review, planning, and daily scrum ceremonies
- Understand how the Bank's risk appetite and risk culture should be considered in day-to-day activities and decisions
- Post-secondary degree in relevant STEM discipline (Computer Science, Electrical/Computer/Software Engineering, and Statistics/Mathematics) and/or 2+ year similar hands-on experience preferred
- Experience with machine learning, statistical techniques, as well as model testing and validation
- Experience cleaning, transforming and visualizing large data sets working with various data formats unstructured logs, XML, JSON, flat files, audio, image)
- Solid SQL skills for querying relational databases SQL Server, DB2, MySQL)
- Programming skills in Python, Java or Scala
- Hands-on experience with Big Data ecosystem tools Beam, BigQuery, Hive, Spark) preferred
- Expertise with Docker or Kubernetes is an asset
- Experience with various services on Google Cloud Platform is an asset
- Experience deploying models into productions as streaming application, Rest APIs, batch jobs or dashboards is an asset
- Knowledge of industry trends regarding fraud and experience in fraud detection and prevention is an asset
- We have an exciting opportunity to join a team of passionate individuals at Scotiabank that are enthusiastic about building trust and enhancing the financial security of our customers.
- This role will be directly working as a part of a Fraud Management AI lab alongside technology partners to develop and implement innovative MLOps solutions on the cloud to solve the challenging and dynamically evolving fraud business problems.
- This role will position the candidate well for leadership exposure, growth and networking opportunities given the nature of the cross-functional work and the overarching business domains in scope of Fraud Management.
- This role has a flexible hybrid working schedule, with a modernized activity-based in-office ecosystem located at the downtown core of Toronto.
Data Science Senior Manager - Toronto, Canada - Scotiabank
Description
The Data Science Senior Manager role will focus on building and managing end-to-end Machine Learning Operations solutions within a cross-functional Agile Scrum lab for fraud detection and prevention. This role will be supporting machine learning model development, implementation, and monitoring, as well as model governance activities for both in-house and external vendor fraud models.
Is this role right for you? In this role you will:
Do you have the skills that will enable you to succeed in this role? We'd love to work with you if you have:
What's in it for you?