Master's Thesis - AI & Automation for Customised Reporting (12 months)

About the role

Pictet is looking for an EPFL master's student to join its four-person Customised Reporting team in Geneva for a 12-month academic placement starting in February/March 2027. The team produces highly bespoke investment reports and combines business expertise with engineering, automation and applied AI. You will work with the Customised Solutions team and data teams to turn a largely manual production model into scalable, AI-enabled reporting solutions.

Key responsibilities

• Map the end-to-end customised reporting production model, including effort, tools, data flows, controls, risks, dependencies and failure points.
• Identify, assess and prioritise opportunities to industrialise and automate data aggregation, validation and reporting workflows, including the complexity added by external bank data.
• Design solutions with the reporting, solutions and data teams that fit existing architecture, data governance, security and control requirements.
• Build, test and implement practical solutions and AI-enabled use cases for data extraction, anomaly detection, commentary generation and report production.
• Develop End User Computing capabilities that are well documented, controlled and maintainable.
• Deliver working tools and prototypes, a prioritised roadmap, documented procedures, user guidance and team training, plus a structured academic report.

Required profile

• Final-year EPFL master's student eligible for a 12-month academic internship.
• Strong analytical, quantitative and problem-solving skills, able to turn business needs into technical solutions.
• Curious, collaborative and hands-on, with communication skills across business and technology teams.
• Only candidates whose master's degree requires an internship can apply.

Required skills

• Solid programming in Python or an equivalent language, with data manipulation, visualisation and workflow automation.
• Practical knowledge of applied AI, including large language models, retrieval-augmented generation and model evaluation.
• Familiarity with the End User Computing lifecycle; knowledge of Microsoft Copilot, GitHub, Snowflake or cloud-based AI services is an advantage.