Semaphore Solutions

Lead consumer insights and innovation strategies to fuel new product development and strategic planning. | Extensive experience in market research, consumer insights, innovation leadership, and team management. | 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.