Financial Crimes Unit Data & Analytics Tech Consulting Intern - Summer 2027
PwCAbout the role
Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Intern/Trainee Job Description & Summary
The Opportunity
As a Financial Crimes Unit Data & Analytics Tech Consulting Intern, you will engage in projects that focus on data analysis and business intelligence within our Advisory practice. You will support our Financial Crimes Unit, which specializes in AML and sanctions managed services, helping clients navigate complex regulatory landscapes. This role offers a unique chance to apply your analytical skills in a client-facing environment, contributing to the development of innovative solutions that drive compliance and operational efficiency.
As an Intern, you will support teams by participating in projects and learning from experienced professionals. You will gain exposure to PwC's methodologies and practices, focusing on developing your personal brand and understanding the business landscape. This role emphasizes learning and growth, encouraging you to ask questions, take initiative, and contribute to the team's success.
In this role, you will have the opportunity to work within our Data, Analytics & AI competency network, leveraging data science and analytics to help businesses harness the power of data. This experience will provide you with valuable insights into the world of risk and regulatory compliance, setting the stage for a rewarding career in consulting.
Responsibilities
- Supporting teams in data analysis and interpretation to identify patterns and make meaningful conclusions
- Participating in the development of business intelligence solutions using tools like Business Intelligence Development Studio and Oracle Business Intelligence
- Assisting in the creation and maintenance of data pipelines to validate efficient data flow and quality
- Engaging in business case development to align projects with corporate strategy and sustain competitive advantage
- Contributing to the design and implementation of data visualization te
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