Data Scientist, Graphs

Position: Data Scientist, Knowledge Graphs
ABOUT MITHRL

We imagine a world where new medicines reach patients in months, not years, and where scientific breakthroughs happen at the speed of thought.

Mithrl is building the world’s first commercially available AI Co-Scientist. It is a discovery engine that transforms messy biological data into insights in minutes. Scientists ask questions in natural language, and Mithrl responds with real analysis, novel targets, hypotheses, and patent‑ready reports.

OUR TRACTION

• 12X year-over-year revenue growth

• Trusted by leading biotechs and big pharma across three continents

• Driving real breakthroughs from target discovery to patient outcomes.

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. 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.

WHAT YOU WILL DO

• Ingest, harmonize, and version high value public biological datasets such as Cellx Gene, Gemma, ARCHS4, ENCODE, GTEx, TCGA, etc.

• Ingest well maintained peer reviewed knowledge bases including Open Targets, 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

WHAT YOU BRING

Required Qualifications

• Strong experience in data science, bioinformatics, computational biology, or a related field

• Experience working with biological knowledge bases, 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 Have

• 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…