Data Scientist Asset Management

2 days ago

Candiac, Canada Scotiabank Full time
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Purpose
In the role of Data Scientist, you bring a specialized data science background to cyber-fraud threat management. You 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 know the end-to-end process of data exploration and can present and communicate data insights and findings to a range of stakeholders. You support advancing the scientific discovery process, including hypothesis testing, to obtain knowledge to solve cyber-fraud and related business problems. You also provide direct incident support managing tactical analysis in response to account-level attacks. You coordinate and solution rapid data insights to react to complex account-level attacks and contain exposure. In addition, you provide key support to prevention and early-detection strategies with other highly skilled cross-domain data and cyber-fraud professionals.


Accountabilities
• Transforming data and information into insights that inform tactical response to incidents as well as strategic decision-making.
• Working with large volumes of data (structured, unstructured, streaming, and other) using ‘big data’ technologies and techniques.
• Developing robust code including notebook-based workflows and the creation of reusable code packages and libraries, and related version control.
• Employ statistical methods as well as machine learning techniques to enhance insights, patterns, visualizations that improve early-detection of cyber-fraud events.
• Develop and maintain data models and structures to support advancing capabilities, creating and testing alternative methodologies and analysis techniques.
• Support the definition and track key performance indicators (KPIs) related to incident management. Regularly report on team performance and effectiveness.
• Support guiding data analytics objectives and design solutioning to 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. The capabilities created through this roadmap empower our incident response team enabling the best visibility, situational awareness, and use-case automation.
• Support defining the data and technology the Response Analytics and Insights team needs to be successful in prevention and early detection of account-level attacks as well as 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, reoccurring problems, and unrelated threats/vulnerabilities to the appropriate business, risk, or control owners through the team’s Problem Management function.
• Be a champion for 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 your peers and others within the Bank that contribute to the success of our mission.
• Speak internally as a subject matter expert (SME) 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, ensuring specific individual goals, plans and initiatives are delivered in support of the team’s business strategies and objectives. Ensures all activities conducted are in compliance with governing regulation and internal policies, procedures, and standards.

Education / Experience / Other Information
• 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, related Data, Quantitative and/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, social network analysis) and machine learning techniques (such as artificial neural networks, clustering and decision tree learning).
• Familiarity of incident management, threat-intelligence, customer identity and access management (CIAM), and payment card security (PCI DSS) business functions is a definite asset.
• Related cybersecurity industry certifications (ex. 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.

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