Data Scientist, Knowledge Graphs

Mithrl•San Francisco, CA

2d•Onsite

About The Position

We are hiring a Data Scientist, Knowledge Graphs to build and scale the biological knowledge layer that powers the Mithrl AI Co-Scientist. This role focuses on ingesting and harmonizing the world’s most important biological data sources and curating the relationships that allow our system to reason across pathways, targets, diseases, compounds, and multimodal datasets. You will ingest data from public consortia and well maintained peer reviewed sources and unify them into a coherent, versioned knowledge graph. You will identify new node types, define relationship schemas, harmonize variable IDs, and ensure metadata remains consistent across all integrated sources. You will also build automated curation pipelines that expand and refine the knowledge graph using both data driven methods and domain logic. Beyond ingestion and curation, you will create the tools and frameworks that allow users to interact with the knowledge graph and even build their own custom graphs based on the results they generate inside Mithrl. Your work will form the foundation for pathway reasoning, target scoring, evidence aggregation, and multimodal interpretation inside the AI Co-Scientist.

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
Ability to build ingestion pipelines for structured or semi structured biological data
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

Nice To Haves

Experience with graph databases or graph query languages
Experience with KG curation, link prediction, relationship extraction, or graph based ML
Familiarity with multi modal data integration
Previous work on biological or chemical knowledge graphs
Experience with public consortia such as ENCODE, GTEx, TCGA, or ChEMBL, etc.
Prior experience in a tech bio startup or scientific software environment

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
Build 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