Head of Ontology - Platform

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Who You’ll Work With

Growth AI Performance System is Bain’s integrated suite of agentic decisioning tools giving large organizations always-on intelligence across their most important growth decisions. It sits at a rare intersection few organizations can genuinely offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and the commercial stakes of both.

You will own the knowledge and data layer behind Bain’s persistent product team, building software that helps Fortune 100 to 500 companies answer the questions that drive their business: where to invest, where to focus, and how to win. The impact of what you build is direct and immediate, and the suite compounds in value with every release.

The platform has two layers working together: the Agentic Core and three product modules, each solving a distinct commercial problem. Your job is to ensure the knowledge layer underneath every module — the entities, relationships, and taxonomies the platform reasons over — is engineered with the same production rigor as the pipelines and models built on top of it.

This is a startup inside an established company. You get genuine early-stage ownership -- real influence over architecture and product direction -- backed by Bain’s global scale, client relationships, and leadership commitment. The pace of a startup with the runway of a global firm.

We are looking for someone who sits genuinely at the intersection of data engineering and AI engineering — comfortable owning large-scale entity resolution and pipeline infrastructure one day, and the retrieval, embedding, and agent-facing API contracts that let that knowledge power live reasoning the next. This is applied knowledge engineering in production, at scale, with real users depending on what you build. Native AI fluency matters here, and so does the drive to keep learning -- because the field is moving fast and this team is built to move with it.

Where You’ll Fit Within the Team

The Head of Ontology is the most senior knowledge engineering role in Growth AI Performance System and one of the most strategically important. You own the knowledge and ontology standard for every Growth AI Performance System product module. You sit above module-level AI Engineers and Data Engineers on questions of entity resolution, taxonomy, and knowledge architecture — and you are the final word when a modeling or retrieval question depends on how the platform represents the commercial world. You are expected to personally build the hardest parts of this layer, not only set direction for others.

The problems span entity resolution at scale, knowledge graph and taxonomy design, retrieval and embedding architecture, and the data pipelines that keep all of it fresh and correct — hard engineering problems where a wrong call quietly propagates into every downstream model and agent. You must be equally credible reviewing a Spark pipeline and a retrieval-augmented generation architecture, because both are your responsibility.

You will work across the platform and product modules focused on marketing ROI, brand or pricing strategy — ensuring that the semantic layer accurately represents the commercial world those modules are built to reason about. A marketing mix model that miscategorized a media channel, a brand analytics tool that conflates two similar entities, or a pricing engine that cannot resolve a product hierarchy correctly will produce outputs that clients cannot trust. The ontology is what prevents that -- and you are accountable for it.

The outputs here are not a research artifact sitting beside the product — they are the substrate the product reasons on. The standard you hold this layer to is the standard the whole platform’s trustworthiness is measured against.

You will lead the Ontology and Knowledge team -- the AI Engineer and Data Engineer who report into this function -- and collaborate daily with the Tech Lead, the Data Scientists across all three product modules, the Evals Lead, and the Product Manager. You will also engage directly with senior client stakeholders: presenting the platform’s knowledge architecture, explaining how the semantic layer is structured and why, and using those conversations to make the ontology more commercially accurate with every engagement.

What You’ll Do

Knowledge architecture and ontology engineering

• Own the design, build, and governance of the Growth AI Performance System knowledge graph and ontology -- the semantic layer that models commercial entities, market relationships, and domain taxonomies across marketing ROI, brand strategy, and pricing
• Define the knowledge architecture standards the platform builds within: entity naming and resolution conventions, taxonomy structure and versioning, relationship modeling, and how gaps or ambiguities in the data are represented explicitly rather than silently dropped
• Design and implement entity resolution systems at production scale using tooling such as Splink, Zingg, or custom probabilistic matching models -- handling ambiguous, incomplete, and conflicting data sources without propagating those ambiguities downstream
• Build and evolve the graph and semantic store itself (e.g., Neo4j, Amazon Neptune, or RDF/OWL triple stores with SPARQL), balancing expressivity, query performance, and operational reliability at production scale
• Evolve the ontology as the platform scales: managing the backward compatibility of schema changes, coordinating with the AI Engineer and Data Engineer on how updates propagate to downstream product module interfaces

AI engineering: retrieval, embeddings, and agentic integration

• Design the retrieval, memory, and context-engineering layer that lets agentic workflows reason over the ontology reliably: vector/graph/hybrid stores (Pinecone, Weaviate, or equivalent), embedding model selection and refresh strategy, and context/prompt caching
• Own the API and schema contracts through which AI Engineers and agent frameworks (LangGraph, MCP, or equivalent) consume ontology outputs -- ensuring retrieval patterns remain coherent as both the knowledge layer and the agentic core evolve
• Build evaluation harnesses for knowledge-layer quality in the same spirit as model evals: entity resolution precision/recall, taxonomy consistency checks, and regression suites that catch ontology-layer failures before they reach production
• Apply NLP/ML techniques (e.g., spaCy, Hugging Face transformer models) for entity extraction, relation extraction, and ongoing taxonomy enrichment from unstructured client and market data

Data and pipeline oversight

• Own the data pipelines that keep the ontology current: ingestion, transformation, and quality-check pipelines (e.g., Databricks, Airflow, dbt) that feed entity resolution and graph construction
• Define data requirements standards for the knowledge layer: schema specifications, quality thresholds, and the edge cases that cause entity resolution or retrieval to produce misleading outputs
• Partner with platform Data Engineering to diagnose when upstream data issues are driving ontology-layer problems, and own root-cause resolution end to end
• Work with the Data Scientists across all three product modules to ensure the ontology supports the statistical modeling requirements of each module: providing the structured prior knowledge and relationship data Bayesian models need to produce defensible outputs

Team leadership and stakeholder communication

• Lead and develop the Ontology and Knowledge team -- the AI Engineer (Data Ontology) and Data Engineer (Ontology) who report into this function -- providing structured mentorship and creating the conditions for both to take on progressively more ownership
• Collaborate daily with the Tech Lead on the architectural interface between the knowledge layer and the Agentic Core, and with the Evals Lead to define evaluation criteria for ontology quality
• Engage directly with senior client stakeholders including commercial leadership -- presenting the platform’s knowledge architecture and using client feedback as a systematic input to ontology development
• Use AI-assisted engineering tools (e.g., Copilot-style coding assistants, LLM-assisted schema and entity-resolution tooling) to accelerate ontology development -- as a productivity multiplier, not a substitute for engineering rigor

About You

Must-have — Technical

• Strong hands-on engineering ability spanning both data engineering and AI engineering: comfortable building production pipelines (Spark/PySpark, Airflow, dbt, or equivalent) and production retrieval/agentic systems (vector stores, embedding pipelines, agent frameworks) in the same role
• Deep expertise in knowledge representation and ontology design: graph databases (Neo4j, Neptune) or RDF/OWL semantic stores, and the judgment to trade off expressivity, scalability, and operational reliability
• Experience designing and implementing entity resolution systems at scale (Splink, Zingg, or custom probabilistic matching) that handle ambiguous and conflicting data without silently propagating errors
• Working fluency in how knowledge graph outputs are consumed by agentic systems: retrieval patterns, indexing strategies, and the API/schema conventions that let AI Engineers build against the knowledge layer reliably
• Experience building evaluation and data-quality frameworks for a knowledge or data layer: what entity resolution correctness means in practice, and how to build automated tests that catch failures before production
• Native AI fluency: active use of AI-assisted engineering tools to accelerate ontology and pipeline development, with demonstrated ongoing learning at the intersection of knowledge engineering and generative AI

Strong plus — Technical

• Experience with NLP/ML entity and relation extraction (spaCy, Hugging Face transformers) for automated taxonomy enrichment from unstructured data
• Direct experience with retrieval-augmented generation (RAG) architecture and embedding model selection/evaluation in a production setting
• Understanding of how ontological structure supports statistical modeling: how relationship data and structured priors feed Bayesian or other inference-based models
• Familiarity with data governance and privacy principles at the intersection of client commercial data and proprietary organizational knowledge

Must-have — Experience

• 8 or more years in data engineering, AI/ML engineering, or knowledge engineering roles, with significant hands-on responsibility for systems that production AI or analytics depend on
• Track record of owning a knowledge graph, ontology, or large-scale data platform that a real product depended on -- production-grade, with the reliability, versioning, and governance standards enterprise deployment requires
• Direct experience leading a data or AI engineering team: setting technical standards, mentoring engineers, and creating conditions for the team to take on progressively more ownership
• Experience engaging with senior client or stakeholder audiences in a technical advisory capacity: presenting architecture decisions and translating them into business terms

Strong plus — Experience

• Deep commercial domain knowledge in at least one area relevant to the Growth AI Performance System suite: marketing and media analytics, brand and consumer behavior, or pricing and revenue growth management
• Experience working in or alongside consulting, professional services, or enterprise analytics organizations where knowledge representation quality has direct consequences for client decisions

Education

Bachelor’s degree or higher in computer science, information science, data engineering, or a related field. Advanced degree in a knowledge representation, AI/ML, or semantic web discipline is a strong advantage.

U.S. Compensation Information

Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).

Some local governments in the United States require a good-faith, reasonable salary range to be included in job postings for open roles. The estimated annualized compensation for this role is as follows:

• In Massachusetts, the good-faith, reasonable annualized full-time salary range for this role is between (please, insert salary range); placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level
• In Illinois, the good-faith, reasonable annualized full-time salary range for this role is between $226,000 – $276,750; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level
• Annual discretionary performance bonus
• This role may also be eligible for other elements of discretionary compensation
• 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date

Bain & Company’s comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.

• Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck
• Generous paid time off, including parental leave, sick leave and paid holidays
• Fully vested 401(k) company contribution
• Paid Life and Long-Term Disability insurance

For all other locations, the good-faith, reasonable annualized full-time salary range for this role is commensurate with competitive geographic market rates for this role and will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

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