Trainee, AI Scientist (M&A, Deal Transformation and Post-Merger Integration Advisory)

Design and validate AI use cases: collaborate with M&A practitioners to translate business problems into well-scoped, testable AI applications with clear success metrics and measurable outcomes. Engineer end-to-end AI solutions (core focus): own the technical development of generative AI tools—from prompt architecture and few-shot design through evaluation frameworks, testing, and production deployment. Establish evaluation and quality standards: develop rigorous testing methodologies to assess model outputs, validate accuracy against domain expertise, and measure real-world utility with M&A subject matter experts. Optimize for production: troubleshoot AI solutions in live client engagements, refine prompts and system design based on performance data, and ensure scalability and reliability at scale. Build reusable AI infrastructure: document prompt patterns, evaluation approaches, and architectural lessons learned to create a foundation for rapid iteration and knowledge transfer across the practice. Collaborate across teams: work closely with M&A colleagues, the Swiss AI advisory practice, and Deloitte’s wider AI community to stay current on platform developments and emerging capabilities.

Our Swiss market-leading Deal Transformation and Post-Merger Integration Team advises Swiss Corporate and mid-market clients to generate and create value from their Mergers & Acquisitions, Divestitures, and Carve-outs. With a dedicated team of experts, we deliver integration and separation advisory services, leading complex engagements with our clients, and deploying deep functional expertise in finance, human resources, commercial, supply chain, and technology. At Deloitte, we support our clients on end-to-end M&A advisory, working closely with our colleagues from transaction services, valuations, and corporate finance. As part of the Strategy, Risk and Transactions Business Line, we further collaborate with our Strategy, Sustainability, Regulatory and Forensics and Technology peers to ensure a seamless and value-driven approach to transactions.

• Strong AI fundamentals: hands-on experience with large language models (LLMs), prompt engineering, and generative AI in a professional or research setting. Familiarity with platforms such as ChatGPT, Claude, Gemini, or similar tools is essential.

• Technical depth in AI: demonstrated ability to design prompts, structure few-shot examples, evaluate model outputs, and iterate based on performance metrics. Understanding of LLM behavior, limitations, and best practices is expected.

• Problem-solving and systems thinking: ability to break down ambiguous business problems, design testable hypotheses, structure evaluation frameworks, and iterate methodically based on evidence.

• Learning agility for domain knowledge: motivated to quickly build working knowledge of M&A topics (integration planning, separation, synergies, carve-outs, PMO workflows) to inform AI design decisions.

• Communication across disciplines: able to translate between technical AI concepts and business language, articulate design trade-offs to non-technical stakeholders, and build trust through clarity.

• Ownership and accountability: takes responsibility for solution quality and delivery, proactively identifies technical risks, and drives tasks through to completion with high standards.

• Strong academic background: degree in computer science, data science, AI, machine learning, or related quantitative field. 1–2 years of professional experience with AI/ML systems is an advantage.

• Language skills: fluent English is required; German or French is an advantage.