Senior Data Scientist, Graph ML

Technology Resource Management; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Technology Resource Management seeks a Senior Data Scientist specializing in Graph ML to design and deploy AI-driven solutions for knowledge extraction and graph-based learning. You will build models in Python and work with backend engineers to integrate into production services and APIs.

As a member of the Knowledge Layer team, you will own ML components from experimentation to deployment, translating complex problems into scalable graph-based representations and evolving knowledge graphs and

Qualifications

• Strong Python skills with production-ready ML experience.
• Familiarity with graph-based learning and knowledge graphs.
• Experience building ML models for extraction and/or reasoning.
• Ability to collaborate with backend engineers for API/service deployment.
• Comfort translating complex problems into scalable ML solutions.

Responsibilities

• Design, build, and deploy ML models focused on knowledge extraction and graph learning.
• Evaluate existing models and improve performance.
• Collaborate with backend engineers to integrate solutions into APIs and services.
• Own ML components from experimentation to deployment within the Knowledge Layer team.

Skills

Python

Graph ML

Machine Learning

Knowledge graphs

APIs

Job description

TRM is seeking a Senior Data Scientist specializing in Graph ML to develop AI-driven solutions for crime disruption. Ideal candidates have strong Python skills and experience with ML models and knowledge graphs.

In this role, you'll design, build, and deploy machine learning models that focus on knowledge extraction and graph-based learning. Your work will involve evaluating existing models and collaborating with backend engineers to integrate solutions into production services and APIs.

As part of the Knowledge Layer team, you'll take ownership of ML components from experimentation to deployment. Success in this position means effectively translating complex problem spaces into actionable ML solutions while contributing to the evolution of knowledge graphs and ontologies.