Job Description

We're seeking a Data Scientist for a company that's building the context layer for enterprise AI - one that discovers how organizations actually make decisions, rather than relying on what they document. At the core of its technology is the Context Graph: a living, self-improving representation of business intelligence mined from operational data. You'll work on the algorithms and models that power it, and on making that intelligence available to the next generation of AI agents.

Responsibilities

• Research, develop, and improve the machine learning models that extract semantic signals from structured and unstructured enterprise data sources.
• Apply and advance unsupervised learning techniques including pattern mining, hierarchical clustering, and graph-based methods — to surface meaningful business context at scale.
• Develop NLP and embedding-based approaches to ground unstructured knowledge (documentation, collaboration tools, BI metadata) into the Context Graph.
• Build validation, scoring, and drift detection frameworks to ensure the semantic model stays accurate and current as organizational behavior evolves.
• Collaborate with engineering to bring models into production within an agentic, multi-step reasoning architecture.
• Stay close to the research frontier across semantic AI, agentic systems, and GenAI, and bring relevant advances into the product.

Requirements

• 5+ years of hands-on data science experience shipping production models.
• Strong foundation in unsupervised learning - clustering, pattern mining, graph algorithms.
• Solid experience with NLP pipelines: embeddings, entity extraction, semantic similarity, topic modeling.
• Hands-on experience with large language models, prompt engineering, RAG architectures, and grounding techniques.
• Hands-on experience with agentic AI systems - multi-step reasoning, tool use, agent feedback loops, and context window management.
• Proficiency in Python and the ML/data science stack.
• Comfort with SQL and reasoning about complex query patterns, this is some of the data you'll be working with.

Nice to Have

• Experience with knowledge graphs or ontology modeling.
• Familiarity with enterprise data infrastructure - warehouses, BI tools, semantic layers.
• Background in statistical drift detection or temporal modeling.
• Experience building or evaluating AI agents in enterprise settings.