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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Laboratory Data Ontologist