Senior Forensic Data Analyst - Financial Services

6w1 month ago

London, GB · Full-time · £70,000 – £95,000

About this role

At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

As part of the Forensics & Integrity Services, the Forensic Data Analytics team provides advanced analytics for fraud detection, investigations, financial crime, disputes, and litigations. Develop data solutions to ingest and monitor data for risks like fraud, bribery, money laundering, sanctions breaches, and market abuse. Combine forensic investigation knowledge with data engineering and data science techniques including social network analysis, machine learning, and large language models.

Work with financial services clients, fraud investigators, auditors, lawyers, and regulators in sensitive situations. Handle end-to-end project delivery from data extraction, transformation, loading, analysis, visualization, to deployment. Carry out reactive and proactive analysis of large structured and unstructured datasets using Python, SQL, and Power BI.

Build a career as unique as you are with global scale, support, inclusive culture, and technology. Contribute your unique voice and perspective to help EY become even better. Create an exceptional experience for yourself and a better working world for all.

Requirements

Strong academic qualifications with a degree in a STEM discipline (Computer Science, Engineering, Statistics, Mathematics, etc.) or equivalent work experience
Demonstrable proficiency in SQL, Python, Azure Data Factory and/or Databricks, and visualization techniques
Awareness across other programming languages such as R, C#, JavaScript
Ability to work independently, manage work products
Experience handling structured and unstructured data from various sources
Knowledge of analytic techniques for fraud detection, financial crime investigations
Proficiency in data engineering and data science techniques like investigative data linking and social network analysis

Responsibilities

Work with financial services clients, fraud strategy, fraud analytics, fraud investigators, internal and external auditors, lawyers and regulatory authorities in sensitive situations
Communicate with clients to scope projects and gather requirements
Highlight and explain the outputs of analytics to clients in the context of their business
Deliver end-to-end projects across the full lifecycle including data extraction, transformation, loading (ETL), analysis, visualisation, deployment and client delivery
Handle large amounts of structured and unstructured data from a variety of data sources
Carry out reactive and proactive data analysis of large datasets using Python, SQL and Power BI
Supervise the work of junior team members and ensure quality control of work products
Develop algorithms and solutions to detect, respond, prevent, continually monitor and investigate fraud, bribery & corruption, misconduct and financial crime