Business Analyst, Fraud Data Governance

ScotiabankToronto, ON
Hybrid

About The Position

In the role of Business Analyst on the Fraud Data Governance team, you will play a key role in shaping how critical fraud data is governed, assessed, and trusted across the Fraud Management Group. This is an opportunity to contribute to high-impact data initiatives, strengthen enterprise-aligned data quality capabilities, and help mobilize the Fraud Data Domain in support of strategic business priorities, risk management objectives, and more confident data-driven decision-making.

Requirements

  • University degree with 2+ years’ experience in Fraud and/or Data Analytics related role.
  • Creative, self-starting, results-oriented, and highly motivated, with strong analytical skills and the ability to independently resolve complex business problems.
  • Strong proficiency in Microsoft Excel, PowerPoint, and Word.
  • Excellent knowledge of SQL; knowledge of or willingness to learn Python is required.
  • Excellent English verbal and written communication skills.

Nice To Haves

  • Working knowledge of Power BI is an asset.

Responsibilities

  • Contribute to the overall fraud data governance mandate and strategic objectives by supporting the adoption of enterprise data governance practices, alignment with the Enterprise Data Risk Management Policy and Standards, and continuous improvement of data quality and governance maturity across fraud data domains.
  • Lead high-impact data quality assessments for critical fraud data initiatives, partnering with project teams, technology and business stakeholders to define scope, approach, timelines, requirements, and engagement models while evaluating whether newly ingested or materially changed data is fit for intended business use and aligned to business expectations for effective data risk management and decision-making.
  • Shape the future of fraud data controls by applying SQL and Python to design, automate, and scale data quality assessment capabilities. Develop reusable scripts, templates, and validation routines that strengthen control effectiveness, improve assessment speed and consistency, and enable more efficient, sustainable data governance across the fraud data domain.
  • Lead the design and deployment of scalable data quality monitoring frameworks and executive-ready dashboards, translating complex fraud data risks into clear rules, thresholds, measures, alerts, and insights that strengthen control effectiveness and enable proactive decision-making across the fraud data domain.
  • Partner closely with technology, project teams, business stakeholders, and governance partners to drive data quality issues from identification through remediation, coordinating impact assessment, prioritization, escalation, post-remediation validation, and transparent reporting.
  • Produce clear, defensible, and executive-ready reporting that summarizes data quality scope, methodology, results, issue status, residual risks, and recommended next steps, helping stakeholders understand data quality impacts and make informed remediation and risk management 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. Ensure all activities conducted are in compliance with governing regulation and internal policies, procedures, and standards.
  • Understand and apply the Bank’s risk appetite and risk culture to day-to-day activities and decisions.
  • Champion a high-performance environment and contribute to an inclusive work environment.

Benefits

  • Continuous learning and advancement via workshops with external providers, courses, and conferences.
  • A culture that promotes teamwork and cross-functional collaboration to achieve business goals.
  • Inclusive workplace that values diversity of thought, background, and experience.
  • Opportunity to contribute to high-impact, high-visibility fraud data initiatives with meaningful business outcomes.
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