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.
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