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Laboratory Data Ontologist
Remote | Canada or US
Overview

We are seeking a Laboratory Data Ontologist to strengthen the semantic foundation of our platform and the client-specific ontologies deployed on top of it. Labbit is built on a typed entity graph - samples, containers, locations, instruments, pools, and their provenance - and every implementation is, at its core, a modelling exercise: mapping a client's scientific and operational vocabulary onto that graph without losing fidelity.

This role sits within our Advisory group. You will spend ~50% of your time billable on client implementation projects as the modelling lead, and the remaining ~50% on internal stewardship - evolving the core ontology, codifying modelling practice, and supporting Sales pursuits. This is not a data engineer role and not a solutions architect role. You are a modeller - steeped in information theory, taxonomy design, and ontology engineering - whose primary deliverables are client and platform ontologies, reference models, and the standards that govern them.

Why This Role Matters

Our platform's differentiator is a configurable, versioned entity graph with immutable lineage. That model is only as valuable as the discipline behind it:

• Advisory engagements deepen when modelling is treated as a first-class deliverable rather than a byproduct of configuration.
• Sales wins when we can quickly show a prospect their world represented cleanly in our model.
• Implementation delivers faster when client vocabulary maps to reusable patterns instead of bespoke types.
• Platform evolves coherently when extensions across clients are legible as variants of shared abstractions rather than divergent one-offs.

Housing this role in Advisory keeps the practitioner close to real client problems - the billable work is where modelling craft is sharpened - while the non-billable half compounds those learnings into shared assets the whole company draws on.

What You Will Do

1. Lead Modelling on Client Engagements (~50% billable)

• Serve as the modelling lead on Advisory and Implementation engagements where ontology depth is the critical risk
• Run discovery sessions to elicit and structure client domain models
• Produce target ontologies - entity types, controlled vocabularies, field taxonomies, workflow decompositions - as billable deliverables
• Review changeset designs for modelling quality alongside implementation engineers
• Coach client counterparts on stewardship of their own model post go-live
  

2. Steward the Core Ontology

• Own the conceptual model behind Labbit's base entity types (@Sample, @Container, @Location, @Instrument, @Reagent, @Pool) and their inheritance semantics
• Maintain design principles for when to extend a base type vs. introduce a new one
• Review proposed changes to the base ontology for coherence, minimalism, and long-term extensibility
• Curate the shared reference/IRI namespace so aliases remain meaningful across changesets and clients
  

3. Codify Modelling Practice Across Advisory

• Author internal standards for taxonomy design, controlled vocabulary governance, and ontology versioning
• Identify reusable extension patterns across client engagements and promote them into shared libraries
• Establish review rituals so modelling decisions are traceable and reversible
• Train Advisory and Implementation staff in applied ontology techniques
• Build a shared library of domain reference models for our priority verticals (QC manufacturing, clinical genomics, CGT, stability)
  

4. Support Sales

• Join late-stage sales cycles to lead ontology discovery sessions with prospects
• Produce lightweight target models that demonstrate fit without over-committing to configuration
• Translate prospect terminology (assays, panels, batches, lots) into our model in real time during demos
  

5. Inform Platform Direction

• Surface modelling gaps discovered across client work as candidate platform investments
• Advise Platform Engineering on schema evolution semantics (changeset migrations, deprecations, aliasing)
• Contribute to decisions about first-class vs. reference-data entities, computed fields, and graph traversal features
  

What You Will Not Do

• Own application development or feature delivery
• Serve as project manager or delivery lead on client engagements
• Replace implementation configuration engineers or platform engineers
• Build a parallel modelling framework outside our changeset system
  

Qualifications

Required

• Strong grounding in information theory, formal ontology, or knowledge representation (academic or applied)
• 5+ years working with structured domain models - taxonomies, controlled vocabularies, ontologies, or graph schemas - in production settings
• Fluency with at least one modelling formalism (OWL/RDF, property graphs, UML class models, ISA-Tab, or comparable)
• Demonstrated ability to elicit domain knowledge from subject-matter experts and translate it into a coherent model
• Comfort in a client-facing, billable advisory context - including scoping deliverables, running workshops, and defending modelling decisions to technical and non-technical stakeholders
• Comfort reading and reasoning about configuration-as-code artifacts (JSON schemas, BPMN, expression languages)
• Excellent written communication - you will produce reference models, standards, and documentation that others rely on
  

Strongly Preferred

• Experience in laboratory informatics, life sciences, or another regulated scientific domain (QC manufacturing, genomics, clinical diagnostics, CGT)
• Familiarity with LIMS, ELN, or scientific workflow platforms and their data models
• Experience with versioned schema evolution and immutable/provenance data models
• Exposure to regulated environments (21 CFR Part 11, GAMP5) and their implications for schema governance
• Prior consulting or professional services experience with utilization targets

To further support our team, we offer the following benefits:

• Competitive vacation
• Flexible health spending account / Health Insurance
• RRSP / 401 K matching
• Annual professional development budget
• The expected salary range for this role is:$150,000 - $190,000 CAD or USD
  Actual compensation may vary based on experience, domain expertise, and geographic location.