Full Stack Data Scientist

We’re looking for a Full Stack Data Scientist / Machine Learning Engineer to join our Knowledge Layer team, a group focused on extracting, structuring, and analyzing knowledge from large-scale, unstructured data. This team sits at the intersection of knowledge graphs, entity resolution, graph extraction, and graph analytics, and plays a critical role in powering TRM’s core intelligence products.

While the team has strong backend and graph engineering expertise, we are looking for someone to be the machine learning and data science voice in the room — bringing practical ML expertise, especially in knowledge extraction and graph-based ML, to help accelerate and scale our capabilities.

This is an ideal role for someone who enjoys operating end-to-end: from model selection and experimentation, to productionizing ML systems and APIs.

The impact you will have:

• Design, build, and productionize machine learning models focused on:
  • Knowledge extraction from unstructured data (e.g., NER, entity linking)
  • Graph-based learning and inference
  • Entity resolution and relationship discovery
• Evaluate and leverage existing ML models and frameworks to solve real-world problems efficiently
• Partner closely with backend and graph engineers to integrate ML models into production services and APIs
• Contribute to the design and evolution of knowledge graphs and ontologies
• Perform exploratory data analysis (EDA) to inform modeling decisions and system design
• Own ML components end-to-end, including experimentation, evaluation, deployment, and iteration
• Help shape best practices for applied ML within the Knowledge Layer team

What we’re looking for:

• 5+ years of experience in data science, machine learning engineering, or applied ML
• Strong programming experience in Python
• Hands-on experience building, training, or deploying machine learning models in production
• Familiarity with NLP or information extraction techniques, such as Named Entity Recognition (NER), text classification, or embedding-based approaches
• Experience or strong interest in knowledge graphs, graph data, or graph-based ML
• Solid software engineering fundamentals, including building and maintaining APIs or services
• Ability to translate ambiguous problem spaces into practical ML solutions
• Strong communication skills and comfort collaborating with engineers across disciplines