We are looking for an experienced Knowledge Engineering Manager to join our Knowledge Representation team. You will be reporting to the Director of Engineering, Data & ML. Using your specialism in knowledge engineering you will spearhead our team towards groundbreaking advancements in knowledge engineering, representation and management. The ideal candidate will be deeply versed in the intricacies of knowledge graphs, graph databases, Knowledge representation techniques, and ideally experienced in the application of graph data science for insightful knowledge extraction and enrichment.

The most successful candidates for this role will be experienced knowledge engineers who have remained hands-on, are most comfortable providing technical leadership and delivering complex knowledge engineering solutions such as knowledge graphs. This role is perfect for a leader who is technically adept and passionate about guiding a team toward innovative solutions in how we represent knowledge. The successful candidate will not only be a people and technical leader, but also a mentor, coach, and a role model in our organisation.

You Will:

Be a people leader of a small (approx 4-6) team of knowledge engineers and data engineers.
Be hands-on as needed in coding, data modelling, as well as participating in system design, code pairing, PR reviews, building data pipelines, and writing TDDs (technical design documents).
Own and drive execution of the technical roadmap for your team in line with the technical and product roadmaps.
Provide engineering/technical leadership on Knowledge Engineering projects that contribute to the data in BenchSci’s Knowledge Graph.
Be responsible for building and maintaining BenchSci’s knowledge graph, including our biological ontologies that form part of it.
Lead the harmonisation and integration of diverse biological ontologies into a cohesive knowledge base, utilising standards like RDF (Resource Description Framework), OWL (Web Ontology Language), and technologies like Neo4j.
Advocate for and implement leading graph database technologies, as well as RDF Stores and Triple-stores where relevant, to construct scalable, performant and robust systems.
Work closely with senior and lead engineers within your team, and on other teams, to ensure alignment on technical solutions and delivery.
Liaise closely with stakeholders from other functions including product, science and project management.
Help ensure the adoption of engineering best practices and state-of-the-art knowledge engineering approaches at BenchSci.Uphold best practices in data modelling, representation, and management.
Drive agile practices within the team, and lead certain agile rituals.
Take a leadership role in our recruiting, hiring, and onboarding processes.
Provide mentorship and carry out regular 1:1 meetings with direct reports.Work with your team to continuously drive improvements in ways of working, productivity and quality of work product

You Have:

5+ years hands-on experience working in knowledge engineering, some of which is in the biological or science domains.
3+ years in technical leadership roles.
2+ years of experience working as a knowledge engineering manager.
A Master’s or PhD in Computer Science, Bioinformatics, or a closely related field, with a strong emphasis on knowledge engineering, possibly also including machine learning.
A proven track record technically leading the delivery of complex knowledge engineering projects with high-performing teams leveraging state-of-the-art technologies and techniques.
Have remained technically hands-on and have maintained a high cadence of code contributions over the last 12 months.
Extensive background in knowledge engineering with a proven track record building and deploying large scalable performant knowledge graphs using graph databases and associated technologies (e.g., Neo4j, Amazon Neptune, TigerGraph, JanusGraph, ArangoDB, and OrientDB).
Deep understanding of when and how to deploy different knowledge graph-related technologies such as labeled property graphs, semantic networks, RDF, and RDFS.
Proficient in various knowledge representation techniques such as ontologies, taxonomies, and frames.
Experience developing or extending ontologies to model domain knowledge in a structured form with an understanding of ontology languages such as OWL (Web Ontology Language).
Domain expertise working in knowledge acquisition of biological data and experience working with biological ontologies (e.g. Mondo, ChEBI, KEGG, UniProt, Reactome etc).
Familiar with mid-level biological ontologies, such as BioLink, and how they can be leveraged to integrate (disambiguation, canonicalisation, standardisation) disparate biological ontologies.
Extensive skills in data modelling in graphs and relational databases, as well as graph and relational database design and management.
Exceptional programming skills, predominantly in Python, with exposure to other languages, along with graph querying languages such as Cypher and SPARQL.
Outstanding leadership qualities, coupled with a passion for mentoring and advancing a team of talented engineers.
Well-versed in Agile software development methodologies and practices.
Outstanding verbal and written communication skills. Can clearly explain complex technical concepts/systems to engineering peers and non-engineering stakeholders alike.
A growth mindset that ensures you’re up-to-date with state-of-the-art and cutting-edge advances related to knowledge engineering, and are actively engaging with the relevant tech communities.

Nice to have:

Knowledge of how to leverage ML, Natural Language Processing (NLP) and LLMs for knowledge discovery and acquisition to build knowledge graphs from unstructured data.
Familiar with state-of-the-art approaches and techniques for generating graph embeddings, and vectorization of knowledge graphs.
Knowledge of how to leverage ML techniques, and LLMs (including RAG) for understanding and extracting data in knowledge graphs.
Have worked alongside machine learning engineers carrying out in-graph machine learning on knowledge graphs you have constructed.
Familiarity with how to maximize knowledge discovery and to enrich knowledge graphs (KG) by reasoning over and inferencing from existing KG data using graph data science (GDS), graph machine learning (GML), and Graph Neural Networks (GNNs) approaches.

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