Laboratory Data Ontologist (Remote)

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.