Lead Applied Scientist (m/w)

Job Informationen

Location: Zug, hybrid Workload: Full-time

Your tasks:

• Design and deploy semantic chunking systems for lengthy, non-uniformly structured legal, tax, and accounting documents
• Build document enrichment pipelines that identify document types, jurisdictions, legal concepts, entities, parties, and other domain-specific metadata
• Develop hierarchical and multi-label document classification systems using both standard and customer-defined taxonomies
• Build LLM-based and traditional NLP information extraction pipelines that identify entities, relationships, citations, references, and key concepts from unstructured content
• Develop knowledge graph construction systems that extract, normalize, connect, and enrich entities, legal concepts, citations, and relationships across large document collections
• Design systems that identify, interpret, and extract insights from complex tabular data embedded within legal, tax, regulatory, and accounting documents
• Create document intelligence capabilities that support downstream search, retrieval, RAG, and agentic AI workflows
• Design robust evaluation frameworks for document understanding systems using expert annotations, synthetic datasets, and production metrics
• Lead technical decisions on document analysis architectures, chunking strategies, extraction methodologies, classification approaches, and knowledge representation frameworks
• Partner closely with engineering teams to deliver scalable, reliable, and production-ready AI systems
• Provide technical leadership and input into AI strategy, platform capabilities, and long-term roadmap decisions
• Mentor applied scientists and machine learning practitioners across the organization

Your profile:

You're a fit for the role of Lead Applied Scientist, Document Understanding if you have:

• PhD in Computer Science, AI, NLP, Machine Learning, Information Retrieval, or a related field, with demonstrable post-degree industry experience developing and deploying document understanding systems at scale.
• Hands-on depth across document analysis, information extraction, classification, knowledge representation, evaluation, and production deployment.
• Successfully taken advanced NLP and AI capabilities from research through production and understand how to transform complex, unstructured content into structured knowledge that powers search, retrieval, reasoning, and intelligent workflows.
• A collaborative mindset where you can mentor and measure success by what ships and performs in production.
• Experience of building production document understanding systems that go beyond basic OCR or document parsing and deliver measurable business impact.
• An understanding of how to transform large collections of unstructured documents into structured knowledge assets, building knowledge graphs from real-world content and using them to improve retrieval, reasoning, and AI workflows and extracting meaningful insights from both natural language and complex tabular content.

Benötigte Skills

• Senior
• Support
• Machine Learning
• Embedded
• Master