[Hiring] Laboratory Data Ontologist @Semaphore Solutions

Role Description

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.

What You Will Do

• 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.
• 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.
• 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).
• 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.
• 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

• 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.

Benefits

• Competitive vacation.
• Flexible health spending account / Health Insurance.
• RRSP / 401 K matching.
• Annual professional development budget.

Salary

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.