Data Scientist, Fraud Threat Management

About the role

As a Data Scientist, you will bring a specialized data science background to cyber-fraud threat management. You will find and interpret rich data sources, merge data sources, and use data modeling and analysis techniques to create actionable insights relevant to cyber-fraud. You will support the end-to-end process of data exploration and present findings to a range of stakeholders. You will also help advance the scientific discovery process, including hypothesis testing, to solve cyber-fraud and related business problems.

This role provides direct incident support by managing tactical analysis in response to account-level attacks, coordinating rapid data insights, and helping contain exposure. You will also support prevention and early-detection strategies with highly skilled cross-domain data and cyber-fraud professionals.

What you’ll be doing

• Transform data and information into insights that inform tactical response to incidents as well as strategic decision-making.
• Work with large volumes of data, including structured, unstructured, streaming, and other data, using big data technologies and techniques.
• Develop robust code, including notebook-based workflows, reusable code packages and libraries, and related version control.
• Use statistical methods and machine learning techniques to enhance insights, identify patterns, create visualizations, and improve early detection of cyber-fraud events.
• Develop and maintain data models and structures to support advancing capabilities, creating and testing alternative methodologies, and improving analysis techniques.
• Improve team-level processes that support analytics and insight.
• Support the definition and tracking of key performance indicators related to incident management, and regularly report on team performance and effectiveness.
• Support data analytics objectives and design solutions to help minimize cyber-fraud events.
• Support the delivery of a roadmap that creates new capabilities and capacity for early detection and response to cyber-fraud incidents, enabling improved visibility, situational awareness, and use-case automation.
• Help define the data and technology needed for Response Analytics and Insights to be successful in prevention and early detection of account-level attacks, while advancing capabilities and performance.
• Support future direction on data strategy, including sources, data design, data integrity, and data and analytics tools.
• Support the identification and escalation of systemic issues, recurring problems, and unrelated threats or vulnerabilities to the appropriate business, risk, or control owners through the team’s Problem Management function.
• Champion a data-driven culture for Incident Management.
• Informally mentor other team members in Response Analytics and broader Incident Management.
• Regularly recognize and reinforce high-quality work and behaviours of peers and others within the Bank that contribute to the success of the mission.
• Act as a subject matter expert in big data as it relates to cyber-fraud prevention and response.
• Understand and apply the Bank’s risk appetite and risk culture to day-to-day activities and decisions.
• Contribute to the overall success of the Global Fraud Management function by ensuring individual goals, plans, and initiatives are delivered in support of the team’s business strategies and objectives, while ensuring compliance with governing regulations and internal policies, procedures, and standards.

What you bring to the role

• 3+ years of data science or machine learning/engineering experience delivering high-quality analytics solutions.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, or a related data, quantitative, or engineering field.
• Expertise in data management best practices and deriving insights from big data through a combination of on-premises and cloud tooling.
• Strong knowledge of math, probability, statistics, and algorithms.
• Skilled in using statistical methods, such as boosting, generalized linear models/regression, random forest, and social network analysis, as well as machine learning techniques, such as artificial neural networks, clustering, and decision tree learning.
• Familiarity with incident management, threat intelligence, customer identity and access management, and payment card security business functions is a definite asset.
• Related cybersecurity industry certifications, such as CISSP, CISM, CISA, GCIH, or similar, are an asset.
• Bilingual in Spanish is an asset.

Working conditions

• Work in a standard office-based environment; non-standard hours are a common occurrence, including on-call incident management support.
• Some global travel may be required.