Barclaysposted 26 days ago
Full-time • Mid Level
Wilmington, NC
Credit Intermediation and Related Activities

About the position

Join Barclays as a Fraud Analyst, AVP. You will be using innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves, leveraging these insights to inform strategic decision-making, improve operational efficiency, and drive innovation across the organization. We're seeking a passionate self-starter with expertise in fraud risk management, and data analysis, who's ready to take ownership of high-impact projects.

Responsibilities

  • Identification, collection, extraction of data from various sources, including internal and external sources.
  • Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
  • Development and maintenance of efficient data pipelines for automated data acquisition and processing.
  • Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
  • Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
  • Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.

Requirements

  • Experience with fraud risk management and strategies.
  • Data analysis for identifying and mitigating risk.
  • Effective communication and collaboration with internal stakeholders.
  • Strategic problem-solving and decision-making.
  • Adaptability and a cautious approach to emerging fraud trends.
  • Advanced knowledge of fraud detection technologies.
  • Leadership qualities and a considerable, self-starting attitude.

Nice-to-haves

  • Experience in governance and fraud-related reporting.
  • Ability to advise and influence decision making.
  • Experience in policy development and operational effectiveness.
  • Experience leading a team performing complex tasks.

Job Keywords

Hard Skills
  • Data Extraction
  • Data Pipelines
  • Machine Learning
  • Operational Efficiency
  • Risk Mitigation
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