The Data Analyst will work with staff in our Advanced Analytics team on client facing projects, analysing structured data. Key responsibilities will include the following: • Working with teams on complex open-ended investigative projects to discover anomalies, unusual behaviours/trends, and to measure risks for the purposes of forensic and regulatory investigations, compliance, and litigation work. Example areas of work will include fraud, bribery and corruption, sanctions, money laundering, data governance and privacy, market abuse, insider trading and much more. • Assisting with preparing project reports, presentations, visualisations, and technical documentation. Essential requirements: • Experience in writing SQL queries, PL-SQL and T-SQL statements to analyse and produce insights from both Oracle and MS-SQL server databases: • Writing complex (and efficient) queries to analyse large tables (e.g. 50M+ rows) • Loading of large volumes of data into the databases and conducting associated activities such as checking data integrity and quality • Experience in writing Python scripts to: • Wrangle data such as XML, log files • Assist with loading data into databases • Conduct data analysis e.g. using Pandas • Ability to collaborate well within small teams on fast moving projects, working efficiently to deadlines • Ability to communicate both written and verbally in a clear, articulate fashion to internal stakeholders and external customers • Commitment to obtaining outstanding results, delivering high quality work with a client centric mindset Desired requirements: • Understanding of financial and accounting system data (e.g., ERP, trading, compliance systems and CRM). • Experience writing queries in a variety of flavours of SQL such as PostgreSQL, MySQL. • Experience of using data visualisation tools such as PowerBI, Tableau, Spotfire, Data Studio to produce insightful dashboards and visualisations. • Experience of working on computing environments in Azure, AWS and GCP. • Experience of working in litigation, investigation or dispute contexts: conducting complex, defensible data discovery, collection, ingestion, and analysis. Additional desired skills will include the following: • Experience of Financial Crime analytics use cases for detection and prevention, including the following: • Experience of conducting model validations, controls testing and screening/monitoring system tuning and optimisation. • Development of machine learning and network analysis models to detect fraud and/or money laundering.