Advisory - Consulting, Forensic Data Analytics | Graduate Associate 2027

Job Description

KPMG's Forensic practice consists of experienced professionals with a wealth of experience in investigations, AML/CTF, data analytics, FATCA/CRS, forensic technology and disputes. We have helped some of the world's biggest companies on some of the most important matters of public interest in Singapore.

We help clients deal with:

• Fraud, misconduct and corruption investigations
• Assess and improve client’s AML controls and financial crime compliance
• Commercial disputes
• Regulatory compliance
• Managing risk on buying or selling a business, intellectual property and contract governance
• Preventing and detecting fraud and financial crime

We also deploy technology tools to help clients deal effectively with large amounts of data and documentation, to manage and disclose important material or highlight fraud, weaknesses and business opportunities from within corporate data.

Our Associates will be part of our Investigations and Disputes Advisory, AML, Data Analytics or Forensic Technology service lines. Associates can expect to:

• Gain hands-on experience in Forensic engagements which can range from fact-finding reviews into allegations of fraud and misconduct, assessing AML frameworks and systems, assisting in customer due diligence remediation and backlog reviews for transaction monitoring and screening alerts, and supporting complex investigations through digital forensic procedures and advanced data analytics
• Grow your network through participation in Forensic practice development with senior management, including drafting proposals, thought leadership and coordinating team building activities
• Gain insights into the methodology and approach used in conducting Forensic engagements
• Obtain an understanding of stakeholder management and project delivery
• Take responsibility in engagement planning and keep senior team members informed of significant developments and progress of the engagement
• Learn and understand how to build relationships with client contacts and maintain these relationships after the work has been completed

As an Associate in the Forensic Data Analytics service line, your scope of work will include:

• Building both supervised and unsupervised machine learning models using programming language such as Python
• Performing rule-based analysis to ascertain data integrity and detect anomalies using SQL
• Assisting in Data Analytics engagements, including data exploration, data processing and data analysis
• Assisting in the development and deployment of analytical solutions, suited for each client's requirements
• Drafting reports and/or creating dashboards using reporting tools such as PowerBI or Tableau, to document findings
• Documenting processes and work done clearly, such as how-to guides, technical notes and training materials
• Supporting the development, testing and deployment of internal system initiatives

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We look for individuals with the below backgrounds:

• Undergraduate degrees with excellent academic records, in computer science, digital forensics, data analytics or related degrees.
• Be able to use forensic software to identify, collect, preserve and analyse electronic data from laptops, desktops, servers, cloud, backup tapes and mobile devices.
• Proficient working knowledge in evidence handling, chain-of-custody, acquisition techniques, analysis of digital evidence, forensic lab practices and procedures.
• Ability to code in any popular language is an advantage. This will help you in automating some of the routine tasks. Knowledge of database management and SQL is preferred.
• Good understanding of Microsoft Windows, Macintosh and other Operating Systems (OS). You should be able to understand and translate the artefacts extracted from the OS into investigation outcomes supported by detailed documentation.
• Any certifications in the field of cloud technologies, computer forensics and/or e-discovery will be an advantage.
  
  
  This Graduate Associate role is open to candidates whose graduation date is expected to be between January 2026 and September 2027