Senior Full Stack Data Scientist, NLP

Skills

About the Role

You will design, build, and productionize machine learning models focused on knowledge extraction from unstructured data (NER, entity linking), graph-based learning and inference, and entity resolution and relationship discovery. You will evaluate and leverage existing ML models to solve real world problems, and you will integrate ML models into production services and APIs. You will help design and evolve knowledge graphs and ontologies, perform exploratory data analysis to inform modeling decisions, own ML components end-to-end from experimentation through deployment, and help define best practices for applied ML.

Requirements

• 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.

Responsibilities

• Design, build, and productionize machine learning models focused on knowledge extraction from unstructured data (NER, entity linking), graph-based learning and inference, and entity resolution and relationship discovery.
• Evaluate and leverage existing ML models and frameworks to solve real-world problems efficiently.
• Partner 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 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.