ABOUT STRATEGY IN ACTION LTD.

Strategy in Action is a pioneering Strategy-as-a-Service (SaaS) provider revolutionizing how organizations develop and implement strategic initiatives. As the first company to offer comprehensive strategy services through an innovative technology platform, we combine deep management consulting expertise with cutting-edge artificial intelligence. Our mission is to democratize access to world-class strategic thinking by creating intelligent systems that augment human decision-making. Our unique approach integrates traditional strategy frameworks with advanced knowledge representation and large language models, enabling companies of all sizes to benefit from strategic insights previously available only to enterprises with significant consulting budgets.
Job Description

ONTOLOGY & KNOWLEDGE GRAPH DEVELOPMENT (40%)

• Design and develop comprehensive ontologies covering both company-specific strategic elements and industry-sector dynamics, following business strategy frameworks (SWOT, Porter’s Five Forces, BCG Matrix, etc.)
• Create relationship taxonomies that capture complex strategic dependencies into formal knowledge structures
• Implement ontology schemas in Neo4j or similar graph database systems. Create graph algorithms and queries to identify strategic patterns and insights from data
• Build data pipelines to extract, transform, and load strategic data from various sources (e.g. LLMs own knowledge, structured data sources, unstructured documents)
• Balance theoretical rigor with practical applications in ontology design, to be validated with strategy experts

REQUIRED QUALIFICATIONS

• Bachelor’s degree or Master’s Degree in Computer Science, Data Science, Information Science, or related field
• 4-5 years of hands-on experience in Machine Learning, Data Science or Software Development
• Experience with graph database technologies (Neo4j preferred)
• Strong programming skills in Python
• Demonstrated interest in knowledge representation, ontologies, or semantic technologies
• Familiarity with large language models and prompt engineering
• Ability to translate conceptual frameworks into technical implementations

PREFERRED QUALIFICATIONS

• Experience with ontology development tools and languages (OWL, RDF, Protégé)
• Background in NLP techniques for information extraction
• Familiarity with LangChain, LangGraph, LlamaIndex, or similar LLM application frameworks
• Experience with business strategy concepts or frameworks
• Contributions to knowledge graph or ontology projects
• Background in semantic web technologies or linked data principles

TECHNICAL SKILLS

• Programming Languages: Proficiency with Python is mandatory. Knowledge of JavaScript is also beneficial.
• Graph Technologies: Neo4j, Cypher, GraphQL
• Data Engineering: ETL pipelines, data integration patterns
• Machine Learning: NLP, embedding models, text classification
• LLM Integration: Prompt engineering, context management
• Visualization: Graph visualization tools and techniques
• Containerization: Docker, Kubernetes
• Cloud Platforms: GCP familiarity is preferred (alternatively AWS or Azure).

ABOUT THE ROLE

We are seeking an exceptional ML Engineer / Data Scientist to design and implement our next-generation strategic intelligence system. You will play a crucial role in connecting business strategy ontologies and knowledge graphs with large language models like OpenAI, Anthropic and Gemini. This position offers a unique opportunity to work at the intersection of knowledge engineering, artificial intelligence, and business strategy. While prior experience is valued, we’re primarily looking for intellectual horsepower, technical versatility, and genuine passion for knowledge representation systems.

WHAT YOU’LL DO IN YOUR FIRST SIX MONTHS

• Design and implement a core strategic ontology covering fundamental business strategy concepts
• Develop a proof-of-concept knowledge graph for a specific industry sector
• Create the initial integration connecting the knowledge graph with an LLM
• Demonstrate strategic use cases showcasing the system’s analytical capabilities
• Establish technical foundations for ongoing development