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The National Forensics team, within Investigations & Compliance, safeguards and enhances organizational reputations by helping clients navigate disputes and regulatory challenges. We employ an integrated approach to strengthen governance, controls, and data insights, ultimately restoring financial and brand trust.

As a Project Analyst in the National Forensics team, you will be instrumental in supporting data-driven investigations and compliance initiatives. Your responsibilities include cleaning, transforming, and validating datasets using Python and SQL, and writing optimized SQL queries.

You will leverage libraries like Pandas and NumPy for analysis and automation, identifying trends and anomalies. Developing reusable scripts for ETL, reporting, and prototyping is key. You'll utilize AI-assisted coding tools responsibly, critically reviewing and validating AI-generated code before implementation while safeguarding confidential client data. Documenting business rules, assumptions, and workflows is also an integral part of this role.

We are seeking a technically adept Data Analyst with 1-3 years of experience, possessing hands-on proficiency in Python and SQL, and a solid theoretical understanding of Artificial Intelligence and Machine Learning. You must be adept at using AI-assisted coding tools responsibly.

Essential skills include Python (Pandas, NumPy, file handling, automation) and SQL (DML, joins, CTEs, window functions). A foundational knowledge of AI/ML concepts (supervised vs. unsupervised learning, classification, regression) and Generative AI (LLMs, prompt engineering, limitations) is required. Experience with AI-assisted coding tools, including prompt generation, logic assessment, debugging, and testing, is crucial.

Candidates should possess strong analytical and problem-solving abilities, meticulous attention to detail, and clear communication skills. Curiosity in GenAI and emerging technologies, along with adaptability in a fast-paced, project-based environment, is highly valued. Success involves using AI as a productivity enhancer while maintaining ownership of logic, security, accuracy, testing, and explainability.