Associate, Data Analytics

About the Role

FRA’s Forensic Technology practice is a specialist global team at the intersection of data, technology, and high‑stakes financial crime and regulatory investigations. We support multinational corporations, regulators, and legal counsel in navigating complex issues including financial crime, regulatory enforcement, and litigation.

As a Data Analytics Associate, you will work on some of the most sensitive and complex matters, contributing to data‑driven insights that directly inform regulatory outcomes, legal strategies, and executive decision‑making. You will have the opportunity to work with large multifaceted datasets across multiple industries such as Aerospace and Defense, Gambling, Manufacturing, Crypto and Financial Services. Unlike larger firms, you will operate in lean, high‑performing teams with direct exposure to senior stakeholders and subject‑matter experts.

• Design data workflows spanning data collection, ETL, analysis, visualisation, and delivery across structured and semi‑structured datasets
• Apply advanced analytical techniques to identify patterns of fraud, corruption, misconduct, and financial crime
• Implement analytical workflows that are fully reproducible, deterministic, and auditable, ensuring that outputs can be independently validated and withstand scrutiny in regulatory and legal contexts
• Work with complex enterprise data (ERP systems, transactional data, communications, logs) to reconstruct events and generate evidence‑based insights
• Develop scalable analytical solutions using modern data tools and programming languages
• Translate analytical outputs into clear, defensible insights for non‑technical stakeholders including legal counsel, regulators, and senior executives
• Collaborate closely with investigators, forensic accountants, and legal teams on cross‑border matters
• Maintain rigorous documentation of data sources, transformations, assumptions, and methodologies to establish clear audit trails and evidentiary integrity
• Contribute to the development of reusable tools, methodologies, and intellectual property within the practice

You will work with a modern and evolving technology stack, including:

• SQL‑based data platforms (e.g., SQL Server, Postgre
  
  SQL, Oracle, DuckDB)
• Python for data processing, statistical analysis, and automation
• Data visualisation tools (Power BI) for storytelling and reporting
• Professional development environments and tooling (e.g., version control systems such as Git, and modern IDEs such as VS Code) to support controlled, traceable, and reproducible analytical workflows
• Cloud platforms (primarily Microsoft Azure)
• Exposure to machine learning, network analytics, and entity resolution techniques

• Strong academic background in a quantitative or analytical field (e.g., Computer Science, Engineering, Statistics, Mathematics, Economics, or similar)
• Demonstrated proficiency in SQL and at least one programming language (Python preferred)
• Experience working with large, complex datasets and deriving actionable insights
• Experience developing analytics that are reproducible, traceable, and methodologically robust, particularly in environments subject to external scrutiny
• A structured and rigorous approach to problem‑solving, with high attention to detail
• Ability to communicate complex concepts with clarity to both technical and non‑technical audiences
• A proactive mindset, with the ability to operate effectively both independently and within small teams

• Experience in consulting, forensic analytics, or data‑driven investigations
• Familiarity with ERP systems or transactional datasets (e.g., SAP, Oracle, Dynamics)
• Exposure to fraud detection, financial crime analytics, or compliance monitoring
• Experience applying statistical and advanced analytical techniques (e.g., hypothesis testing, regression, anomaly detection, or machine learning)
• Experience working with version control systems (e.g., Git) and structured development environments (e.g., VS Code or similar), with an understanding of their role in ensuring traceability, collaboration, and reproducibility
• Knowledge of data…