Posted May 8, 2026
As a Specialist II, Data Analyst at EY, you will have the opportunity to leverage data-driven insights to support strategic business decisions and enhance operational efficiency. Your role will involve engaging with internal stakeholders to understand business objectives, translating requirements into analytical projects, and driving end-to-end execution with structured problem-solving. You will gather, clean, and validate structured and unstructured datasets from multiple sources, ensuring accuracy and consistency in data processing. Performing deep-dive analysis and statistical modeling will be crucial in identifying trends, correlations, and business drivers for data-backed decision-making. Additionally, you will design visually compelling dashboards and reports using tools like Power BI, Tableau, or similar platforms to simplify complex data for business users. Automation of reporting and analytics workflows using SQL and Python will be essential to reduce manual efforts and enhance data accessibility. Implementing predictive models, clustering techniques, and forecasting algorithms will support business strategy, risk assessment, and operational planning. Key Responsibilities:
Engage with internal stakeholders to understand business objectives and drive end-to-end execution with structured problem-solving. - Gather, clean, and validate structured and unstructured datasets from multiple sources. - Perform deep-dive analysis and statistical modeling to identify trends, correlations, and business drivers. - Design visually compelling dashboards and reports using Power BI, Tableau, or similar tools. - Automate recurring reporting and analytics workflows using SQL and Python. - Implement predictive models, clustering techniques, and forecasting algorithms. - Work closely with data engineers, IT teams, and business stakeholders to enhance data architecture. - Mentor and provide guidance to junior analysts. - Develop and manage AWS data pipelines for fraud-related datasets. - Write production-quality code following best practices. - Create Python-based visualizations to support fraud analytics. - Translate complex fraud analytics into clear narratives for stakeholders. - Own key analytical and data engineering processes supporting fraud detection. Qualifications Required:
Minimum 5 years of relevant work experience, with at least 3 years in advanced data analysis, statistical modeling, or business intelligence. - Bachelors degree in Computer Science, IT, Statistics, or Mathematics. - Proficiency in SQL, Python, and data visualization tools like Power BI or Tableau. - Hands-on experience with core AWS services for data processing. - Understanding of fraud patterns and early-life monitoring techniques. At EY, you will be part of a team that values strong teamwork, work ethic, client centricity, and a commitment to EY values. The company offers a competitive remuneration package, support for flexible working, career development opportunities, and a comprehensive Total Rewards package. Working at EY means being part of a diverse team that aims to build a better working world by providing trust through assurance and helping clients grow, transform, and operate effectively. As a Specialist II, Data Analyst at EY, you will have the opportunity to leverage data-driven insights to support strategic business decisions and enhance operational efficiency. Your role will involve engaging with internal stakeholders to understand business objectives, translating requirements into analytical projects, and driving end-to-end execution with structured problem-solving. You will gather, clean, and validate structured and unstructured datasets from multiple sources, ensuring accuracy and consistency in data processing. Performing deep-dive analysis and statistical modeling will be crucial in identifying trends, correlations, and business drivers for data-backed decision-making. Additionally, you will design visually compelling dashboards and reports using tools like Power BI, Tableau, or similar platforms to simplify complex data for business users. Automation of reporting and analytics workflows using SQL and Python will be essential to reduce manual efforts and enhance data accessibility. Implementing predictive models, clustering techniques, and forecasting algorithms will support business strategy, risk assessment, and operational planning. Key Responsibilities:
Engage with internal stakeholders to understand business objectives and drive end-to-end execution with structured problem-solving. - Gather, clean, and validate structured and unstructured datasets from multiple sources. - Perform deep-dive analysis and statistical modeling to identify trends, correlations, and business drivers. - Design visually compelling dashboards and reports using Power BI, Tableau, or similar tools. - Automate recurring reporting and analytics workflows using SQL and Python. - Implement predictive models, clustering techniques, and f
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