Laboratory Data Ontologist

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 Engine