Data Scientist, Knowledge Graphs

About the role

We are hiring a Data Scientist, Knowledge Graphs to build and scale the biological knowledge layer that powers the Mithrl AI Co-Scientist.

Responsibilities

• Ingest, harmonize, and version high value public biological datasets such as CellxGene, Gemma, ARCHS4, ENCODE, GTEx, TCGA, etc.
• Ingest well maintained peer reviewed knowledgebases including OpenTargets, HPA, and similar resources
• Build automated pipelines to curate and expand relationships inside the knowledge graph
• Define and evolve schemas for node types, relationships, metadata rules, and ontology alignment
• Harmonize variable IDs and metadata fields across all imported sources to create a unified knowledge layer
• Build and maintain versioning, change tracking, and provenance systems for all data and relationships
• Develop the framework that allows users to build custom knowledge graphs from the analyses they run inside Mithrl
• Create features that allow users to explore, query, and interact with their graphs
• Work closely with ML engineers, bioinformatics teams, and discovery application teams to ensure the knowledge graph supports downstream reasoning and analysis
• Validate the correctness, completeness, and integrity of the knowledge graph across releases

Requirements

• Strong experience in data science, bioinformatics, computational biology, or a related field
• Experience working with biological knowledgebases, public datasets, or ontology driven systems
• Familiarity with graph data structures, relationship modeling, and knowledge graph concepts
• Experience harmonizing heterogeneous biological datasets and mapping variable IDs across sources
• Proficiency in Python and scientific computing libraries
• Strong understanding of metadata standards, biological ontologies, and domain logic
• Ability to translate complex biological information into structured, machine readable representations
• Excellent communication skills and comfort collaborating across engineering and scientific teams

Qualifications

• Required: Experience with data science, bioinformatics, computational biology, or a related field
• Required: Familiarity with graph data structures, relationship modeling, and knowledge graph concepts
• Required: Strong understanding of metadata standards, biological ontologies, and domain logic
• Required: Ability to translate complex biological information into structured, machine readable representations
• Required: Excellent communication skills and comfort collaborating across engineering and scientific teams
• Nice to have: Experience with graph databases or graph query languages
• Nice to have: Experience with KG curation, link prediction, relationship extraction, or graph based ML
• Nice to have: Previous work on biological or chemical knowledge graphs
• Nice to have: Experience with public consortia such as ENCODE, GTEx, TCGA, or ChEMBL, etc.
• Nice to have: Prior experience in a tech bio startup or scientific software environment