Head of Ontology - Platform

Bain & Company

3d • $226,000 - $276,750

About The Position

The Head of Ontology is the most senior knowledge engineering role in Bain’s Growth AI Performance System, owning the knowledge and ontology standard across all product modules. The role leads the design, implementation, and governance of the semantic layer—including entities, relationships, taxonomies, and the knowledge graph—that supports products focused on marketing ROI, brand strategy, and pricing. The position requires hands-on ownership of production-scale entity resolution, graph and taxonomy design, retrieval and embedding architecture, and the data pipelines that keep the knowledge layer current and reliable. The role also defines architecture standards, schema and API contracts, evaluation frameworks, and processes for evolving ontology schemas while maintaining compatibility with downstream products and agentic systems.

The Head of Ontology leads the Ontology and Knowledge team, including an AI Engineer and Data Engineer, and collaborates with the Tech Lead, Data Scientists, Evals Lead, Product Manager, and platform Data Engineering. The role engages directly with senior client stakeholders to present the platform’s knowledge architecture and incorporate commercial feedback into ontology development. It is expected to personally build the most difficult parts of the knowledge layer, use AI-assisted engineering tools to improve productivity, and maintain engineering rigor across the platform.

Requirements

• Strong hands-on engineering ability across data engineering and AI engineering, including production pipelines and production retrieval or agentic systems.
• Deep expertise in knowledge representation and ontology design, including graph databases or RDF/OWL semantic stores and the ability to balance expressivity, scalability, and operational reliability.
• Experience designing and implementing entity resolution systems at scale that handle ambiguous and conflicting data without silently propagating errors.
• Working fluency in how knowledge graph outputs are consumed by agentic systems, including retrieval patterns, indexing strategies, and API or schema conventions.
• Experience building evaluation and data-quality frameworks for a knowledge or data layer, including automated tests that catch failures before production.
• Active use of AI-assisted engineering tools and demonstrated ongoing learning in knowledge engineering and generative AI.
• At least 8 years in data engineering, AI/ML engineering, or knowledge engineering, with significant hands-on responsibility for systems that production AI or analytics depend on.
• Track record of owning a production-grade knowledge graph, ontology, or large-scale data platform depended on by a real product, with enterprise reliability, versioning, and governance standards.
• Direct experience leading a data or AI engineering team, setting technical standards, mentoring engineers, and supporting increasing team ownership.
• Experience engaging with senior client or stakeholder audiences in a technical advisory capacity and translating architecture decisions into business terms.
• Bachelor’s degree or higher in computer science, information science, data engineering, or a related field.

Nice To Haves

• Experience with NLP/ML entity and relation extraction, including tools such as spaCy or Hugging Face transformers, for automated taxonomy enrichment from unstructured data.
• Direct experience with production retrieval-augmented generation architecture and embedding model selection or evaluation.
• Understanding of how ontological structure supports statistical modeling, including how relationship data and structured priors feed Bayesian or other inference-based models.
• Familiarity with data governance and privacy principles involving client commercial data and proprietary organizational knowledge.
• Deep commercial domain knowledge in marketing and media analytics, brand and consumer behavior, or pricing and revenue growth management.
• Experience in or alongside consulting, professional services, or enterprise analytics organizations where knowledge representation quality affects client decisions.
• An advanced degree in knowledge representation, AI/ML, or a semantic web discipline.

Responsibilities

• Own the design, build, and governance of the Growth AI Performance System knowledge graph and ontology across marketing ROI, brand strategy, and pricing.
• Define knowledge architecture standards for entity naming and resolution, taxonomy structure and versioning, relationship modeling, and explicit handling of data gaps or ambiguities.
• Design and implement production-scale entity resolution systems using tools such as Splink, Zingg, or custom probabilistic matching models.
• Build and evolve the graph and semantic store, balancing expressivity, query performance, and operational reliability.
• Manage ontology evolution and schema backward compatibility, coordinating updates with the AI Engineer and Data Engineer and downstream product module interfaces.
• Design retrieval, memory, and context-engineering capabilities for agentic workflows, including vector, graph, or hybrid stores; embedding selection and refresh; and context or prompt caching.
• Own API and schema contracts through which AI Engineers and agent frameworks consume ontology outputs.
• Build evaluation harnesses for knowledge-layer quality, including entity resolution precision and recall, taxonomy consistency checks, and regression suites.
• Apply NLP and machine-learning techniques for entity extraction, relation extraction, and taxonomy enrichment from unstructured client and market data.
• Own ingestion, transformation, and quality-check pipelines that keep the ontology current.
• Define data requirements and quality standards for the knowledge layer, including edge cases that can make entity resolution or retrieval misleading.
• Partner with platform Data Engineering to diagnose upstream data issues and resolve root causes end to end.
• Work with Data Scientists across product modules to ensure the ontology supports their statistical modeling requirements and provides structured prior knowledge and relationship data.
• Lead and develop the Ontology and Knowledge team, providing structured mentorship and enabling team members to take on increasing ownership.
• Collaborate with the Tech Lead on the knowledge-layer interface with the Agentic Core and with the Evals Lead on ontology-quality evaluation criteria.
• Engage directly with senior client stakeholders, present the platform’s knowledge architecture, and use client feedback to inform ontology development.
• Use AI-assisted engineering tools to accelerate ontology development while maintaining engineering rigor.

Benefits

• Annual discretionary performance bonus.
• 401(k) plan with an annual employer contribution based on years of service; the contribution is 4.5% and increases after 3 years of service, with full vesting upon start date.
• Bain pays 100% of individual employee premiums for medical, dental, and vision programs.
• Paid time off, including parental leave, sick leave, and paid holidays.
• Paid Life and Long-Term Disability insurance.
• Comprehensive benefits and wellness program.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior