Scientific Knowledge Engineer

Scientific Knowledge Engineer, Ontology & Data Modeling

Budget: up to 170 pln/h

Project Description

About the Role Scientific Knowledge Engineer, Ontology & Data Modeling

This role is responsible for:

• Maximizing the value of data assets over a lifetime

• Translating highly technical information from domain experts into appropriate data models

• Creating ontology and vocabulary structures for indexing and structuring data

Working with:

• Product managers

• R&D subject matter experts

To define:

• Data models

• Ontology

• Standards

• Scientific language into data products

Acting as:

• Voice of “Knowledge base”

• Interoperability/value of asset owner

Key responsibilities include:

• Definition of schemas/ontology and data models of scientific information

• Accountability for quality control and mapping specifications

• Validation and verification of mapping specifications

• Working with Product managers/engineers to convert business needs into deliverable requirements

• Integration of large-scale biology data

• Supporting prediction/modeling of therapeutically relevant protein complex and antigen conformations

• Collaboration with external groups to align data standards with industry/academic ontologies

• Providing subject matter expertise for R&D data

• Translating deep science into actionable insights

• Maintaining documentation of:

  • data standards,

  • ontology decisions,

  • mapping rationale

Duration

• From: 25 May 2026

• To: 25 May 2027

Work Type

• Remote

Must Have

Basic Qualifications

• Masters degree in:

  • Bioinformatics

  • Biomedical Science

  • Biomedical Engineering

  • Molecular Biology

  • Computer Science (with life science focus)

• 6+ years of relevant work experience

• Experience contributing to Knowledge Graph development:

  • entity modeling

  • relationship design

  • schema governance

• Hands-on experience with:

  • Protégé

  • SPARQL

  • OWL

  • SKOS

  • SHACL

  • RML

  • RDF/Turtle

• Knowledge of life sciences ontologies:

  • Gene Ontology (GO)

  • OBO Foundry ontologies

  • CL

  • UBERON

  • HPO

  • MONDO

  • CHEBI

  • EFO

  • CLO

  • MeSH

  • SNOMED CT

  • UMLS

• Familiarity with:

  • linked data principles

  • semantic web technologies

• Experience with:

  • JSON Schema

  • LinkML

• Python

Nice to Have

• Data governance / quality tooling:

  • Ataccama

  • Informatica

  • Talend

  • OpenRefine

  • Great Expectations

  • dbt

• AI / LLM workflows:

  • metadata enrichment

  • entity linking

  • embedding pipelines

  • RAG architectures

• Vector databases:

  • Weaviate

  • Chroma

• Cloud platforms:

  • AWS

  • GCP

  • Azure

• Graph databases:

  • Neo4j

  • Amazon Neptune

  • Stardog

  • GraphDB

  • TigerGraph