Principal Financial Data Ontologist & Taxonomy Architect

We are looking for a Principal Financial Data Ontologist to design and govern the foundational taxonomy, symbology, and entity models that define how we represent global financial markets. This is a hybrid role spanning financial domain expertise, data modeling, classification science, and light technical prototyping.

We are looking for a Principal Financial Data Ontologist to design and govern the foundational taxonomy, symbology, and entity models that define how we represent global financial markets. This is a hybrid role spanning financial domain expertise, data modeling, classification science, and light technical prototyping. You will create academically defensible, globally consistent systems for identifying, naming, categorizing, and relating every instrument, entity, and dataset across asset classes.

This is not a data engineering role, and it's not a product manager role. You will own the conceptual correctness, scientific rigor, and semantic clarity behind the data models and taxonomies we ship.

If you love turning messy, conflicting real-world data into clean, principled, explainable systems, and you want to build the market's most coherent ontology, this role is for you.

• Design and maintain a canonical, cross-exchange symbology for global securities, with deterministic rules for identifiers, listings, cross-listings, derivatives families, and lifecycle events.
• Create academically rigorous classification systems for industries, instrument types, corporate structures, and event categories—formally defined, versioned, citable, and empirically justified.
• Develop and evolve a unified entity and relationship model spanning issuers, securities, people, organizations, funds, indices, derivatives, and other market participants.
• Build robust cross-identifier mapping frameworks across major global identifier standards (ISIN, SEDOL, CUSIP, FIGI, RIC, OCC, futures symbology, and exchange-specific codes), including conflict resolution and lineage tracking.
• Define and govern internal naming standards, metadata conventions, and semantic rules that ensure consistency and clarity across all datasets and data products.
• Establish principled, formal rules for how data elements relate and correspond, enabling reliable interoperability between diverse datasets and preventing ambiguous or incorrect data assumptions.
• Prototype classification logic, normalization rules, and validation algorithms using Python or SQL to test correctness, reproducibility, and practical applicability.
• Evaluate, reconcile, and harmonize incoming datasets from heterogeneous sources using transparent, deterministic, academically defensible methodologies.
• Produce clear, authoritative documentation describing the definitions, rules, and conceptual models behind the identifiers, taxonomies, and ontologies you design.
• Partner with product and data teams to integrate your models into internal data systems and customer-facing data products.

• Deep experience with reference data, symbology, taxonomy, or instrument modeling at a market data vendor, exchange, quant fund, or similar organization.
• Expertise in global identifiers (ISIN, SEDOL, CUSIP, FIGI, RIC, OCC, futures symbology) and how they're assigned, maintained, and resolved.
• Strong understanding of cross-asset market structure, including equity listings, derivatives contracts, corporate actions, index composition, and exchange rules.
• Demonstrated ability to design scientifically rigorous, explainable, and consistent classification systems.
• Comfort writing Python or SQL for prototyping, validation, and data exploration.
• Exceptional written clarity—able to produce precise, formal definitions and explain complex taxonomy decisions.
• Experience building or contributing to security master models, reference data systems, or entity graphs is a plus.
• Familiarity with ontology frameworks or graph modeling (OWL, RDF, Neo4j, knowledge graphs) is a plus.
• Exposure to fundamentals taxonomy (GAAP/IFRS line items) from a metadata or modeling perspective is a plus.
• Experience designing internal data naming conventions, semantic layers, or entitlement systems is a plus.
• Academic…