Our team analyse data for investigations into price fixing, misappropriation of funds, employee misconduct, financial crime and much more. We use the latest data tools and technologies (Spark, Scala, SQL, Python, Alteryx) to solve our clients’ most sensitive and difficult issues. We process data of all types and sizes either on-premise or hosted in the Cloud. We employ a variety of Data Science models and techniques to sift through large amounts data to identify patterns and behaviours of non-compliance or fraud. Outcomes and insights are presented to stakeholders in the form of compelling stories using a variety of visualisation and reporting tools such as Power BI.
What you will be doing
- Working in UK and Ireland (UK&I) or Financial Services (FS), identify, receive and construct large composite data sets.
- Solve client issues by employing the most recent and advanced tools and analytical techniques from rules-based analysis to deep neural networks and natural language processing.
- Design and develop innovative modularised reusable solutions using our cutting-edge platforms and tools.
- Acquire domain knowledge through working with clients in different sectors.
- Become familiar with the Forensics domain (Anti Money Laundering, Counter Terrorism Financing, Sanctions, Fraud, Anti Bribery and Corruption and Compliance).
Programme length
Three years
Professional qualification
SQL, Data Visualisation, Python, PowerBI and more.
Eligibility for applying
To find out more about eligibility requirements and visa sponsorship, please visit the individual programme pages on our website to learn more.
EY recognises that it has clear obligations towards all its people and the community at large to ensure that partners, employees and job applicants are afforded equal opportunities to enter employment and progress within the firm. As an employer committed to establishing an inclusive environment free from unlawful discrimination, the firm promotes the fair treatment of staff members and job applicants regardless of their sex, gender identity, marital or civil partnership status, race, ethnic or national origin, disability, religion, sexual orientation, age or pregnancy/maternity status.
