---
title: 'Data Scientist, Knowledge Graphs at Mithrl'
canonical: 'https://feeny.ai/job/data-scientist-knowledge-graphs-mithrl-san-francisco-zmyfmxnqk200'
type: 'job'
last_seen: '2026-08-30'
---

# Data Scientist, Knowledge Graphs at Mithrl

- **Company:** Mithrl
- **Location:** San Francisco, CA
- **Compensation:** $150k–$200k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-12-30
- **Last confirmed live:** 2026-08-30
- **Apply:** https://jobs.ashbyhq.com/mithrl/69c6cd0f-ef58-4e19-b148-cad568868aee

## Job description

## 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 speaks for itself:

- 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 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

## WHAT YOU BRING

Required Qualifications

- 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 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 startup or scientific software environment

## WHAT YOU WILL LOVE AT MITHRL

- You will build the core knowledge layer that the AI Co-Scientist uses to reason about biology
- Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders
- Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution
- Speed: We ship fast (2x/week) and improve continuously based on real user feedback
- Location: Beautiful SF office with a high-energy, in-person culture
- Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

## About Mithrl

## Company Overview
- **One-liner**: Mithrl builds a scientific decision engine that uses natural language to generate transparent, auditable analysis pipelines for biomedical R&D, accelerating discovery from raw data to actionable insights in minutes instead of weeks.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Vivek Adarsh (Co-Founder & CEO), Shara Balakrishnan (Co-Founder & CTO)

## Core Business
- **Primary industry/industries**: Biomedical research software; AI-driven drug discovery and biomarker analysis
- **Target customers**: B2B; pharma and biotech R&D teams (enterprise), discovery researchers across therapeutic areas
- **Mission or purpose statement**: “Help R&D teams go from idea to IND, 50% faster” [mithrl.com/company](https://www.mithrl.com/company)

## Products & Services
- **Mithrl Platform (Scientific Decision Engine)**: An enterprise SaaS platform that takes raw omics and other discovery data, processes it through AI-built custom workflows, and produces defensible, reproducible insights. Users interact via natural language, eliminating manual coding and pipeline setup. [mithrl.com](https://www.mithrl.com/)
- **Eos (mentioned on website)**: Likely a component or interface of the platform (details not fully available).

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Total Funding of USD $4.0M (Seed round, led by Bonfire Ventures, November 2024) [LinkedIn](https://www.linkedin.com/company/mithrl-ai)
- **Notable Investors/Partners**: Bonfire Ventures (lead investor); Board member Brett Queener
- **Growth Signals**: 100% headcount growth year-over-year (from 11 to 20 employees); 15 active job postings; website traffic up 199% yearly; LinkedIn followers up 145.7% year-over-year to ~6,400; recently published a paper on “the Era of the AI Co-Scientist” [LinkedIn](https://www.linkedin.com/company/mithrl-ai)

## Competitive Advantages
- **Speed & Automation**: Reduces typical analysis cycles from weeks to minutes (32x acceleration claimed) using natural language input, without requiring coding skills.
- **Transparency & Reproducibility**: Every step of the pipeline is auditable; results can be mapped to known biology, pathways, and literature, meeting regulatory-quality standards for IND filings.
- **Domain Expertise**: Built by scientists for scientists — founders and team have deep experience in biology, engineering, and AI, targeting the gap between biological intuition and computational insight. [mithrl.com/company](https://www.mithrl.com/company)

## Strategic Focus
- **Current priorities**: Continue scaling the platform to serve more enterprise pharma/biotech clients; accelerate the path from hypothesis to IND decision; expand capabilities across multiple therapeutic areas and data types (omics and beyond).
- **Direction for growth**: Growing the engineering and scientific AI team (multiple open roles), increasing market presence in drug discovery and biomarker identification.

## Why Work Here
- **Culture**: The company was built “by scientists, for scientists” — a team of biologists, engineers, and AI researchers who understand the pain of analysis backlogs. Emphasis on moving fast, using the latest AI tools, and directly impacting real drug discovery timelines. [builtinsf.com](https://www.builtinsf.com/company/mithrl)
- **Work Policy**: In-office (HQ in San Francisco’s 44 Montgomery St); all employees work from physical office. [builtinsf.com](https://www.builtinsf.com/company/mithrl)
- **Notable Perks & Engineering Culture**: Roles span Forward Deployed Engineer, ML Engineer, Bioinformatics Engineer, Data Engineer — indicating a tight connection between product engineering and scientific end-users. Rapid growth (100% headcount increase) suggests a high-energy, scaling startup environment. 15 open positions (as of mid-2025) offer significant ownership opportunities.

## Sources
1. [mithrl.com](https://www.mithrl.com/)
2. [mithrl.com/company](https://www.mithrl.com/company)
3. [LinkedIn](https://www.linkedin.com/company/mithrl-ai)
4. [builtinsf.com](https://www.builtinsf.com/company/mithrl)

## Other roles at Mithrl

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- [Application Scientist](https://feeny.ai/job/application-scientist-mithrl-san-francisco-chw2jnarq189) — San Francisco, CA
- [Design Technologist](https://feeny.ai/job/design-technologist-mithrl-san-francisco-4vdb5dv9k4k4) — San Francisco, CA
- [Senior Software Engineer](https://feeny.ai/job/senior-software-engineer-mithrl-san-francisco-2apn6aq88c90) — San Francisco, CA
- [Data Engineer, Scientific Data Ingestion](https://feeny.ai/job/data-engineer-scientific-data-ingestion-mithrl-san-francisco-nf23typhzby9) — San Francisco, CA
- [ML Engineer, Discovery Applications](https://feeny.ai/job/ml-engineer-discovery-applications-mithrl-san-francisco-g36gc1p4xbra) — San Francisco, CA
- [ML Engineer, Biological Analysis & Simulation](https://feeny.ai/job/ml-engineer-biological-analysis-simulation-mithrl-san-francisco-dhbn7wj0zte5) — San Francisco, CA
- [Bioinformatics Engineer, Pipelines](https://feeny.ai/job/bioinformatics-engineer-pipelines-mithrl-san-francisco-spne2m87s85g) — San Francisco, CA
- [Data Engineer, Knowledge Graphs](https://feeny.ai/job/data-engineer-knowledge-graphs-mithrl-san-francisco-b38gr7055zhy) — San Francisco, CA
- [QA Engineer, Scientific Workflows](https://feeny.ai/job/qa-engineer-scientific-workflows-mithrl-san-francisco-nw7h6bfj466w) — San Francisco, CA