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System

Causal, data-driven maps of complex systems

Research & Science

AI ResearchConfirmed live in the last 24 hours
Full-timeNew York, NY, USANo salary listedRemote

About the Job

Requirements

  • A Master's or PhD in Computer Science, Statistics, Biomedical Informatics, or a related field is required.
  • A strong publication record or equivalent applied research experience is required.
  • Deep expertise in machine learning, statistical modeling, or computational methods is required.
  • Experience working with complex, heterogeneous, or graph-structured data is required.
  • The candidate must be able to communicate research findings clearly to technical and non-technical audiences.
  • The candidate must be comfortable operating in a fast-paced startup environment alongside a long research horizon.

Responsibilities

  • Develop and validate novel methods for modeling complex, interconnected systems.
  • Work at the intersection of graph theory, machine learning, and domain-specific knowledge.
  • Collaborate with Data Science and Engineering to transition research findings into production.
  • Publish and present findings that advance the field and the company's mission.
  • Partner with clinical and domain experts to ground research in real-world impact.
  • Contribute to a culture of intellectual rigor, curiosity, and continuous learning.

Desired Qualifications

  • Experience with clinical natural language processing, electronic health record data, or healthcare ontologies such as SNOMED, ICD, and OMOP.
  • Familiarity with knowledge graphs, semantic web technologies, or ontology engineering.
  • Background in causal inference, systems dynamics, or complex adaptive systems.
  • Experience collaborating with clinicians, policymakers, or public health researchers.

About the Company

System

Causal, data-driven maps of complex systems

System, Inc. helps people understand and address complex global problems by turning vast data into system maps. It builds data-driven graphical models that show how factors interrelate—starting with public health topics like COVID-19 and its links to diabetes and obesity—and aggregates thousands of sources, including peer-reviewed research, to illustrate cause-and-effect relationships. The platform is designed for researchers, policymakers, and the public to explore problems, identify intervention points, and make smarter decisions based on how events and conditions influence each other. What sets System apart is its focus on systems thinking and causal inference within a public benefit framework, plus a dedicated platform for visualizing knowledge graphs that connect data to real-world decisions. The company aims to improve collective understanding of complex systems and empower better actions by revealing interdependencies and intervention points across global issues.

Company size
11-50
Company stage
Grant
Total funding
$19M
Headquarters
New York City, New York
Founded
2017

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