Senior Data Scientist
Location: Tel Aviv, Israel (תל אביב) · Category: R&D · Seniority: Senior · Work model: Hybrid · Posted: 2026-03-10
The company
We are a high-growth, venture-backed data intelligence platform building the next generation of semantic and agentic AI infrastructure. Our technology bridges the gap between complex enterprise data warehouses and business execution, enabling teams to query, model, and automate workflows seamlessly.
The Role
Work on the algorithms and models behind a context layer for enterprise AI — a living, self-improving representation of business intelligence mined from operational data rather than from what an organisation documents — 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.
- Build validation, scoring and drift detection frameworks so the semantic model stays accurate and current as organizational behaviour 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 generative AI, and bring relevant advances into the product.
Requirements
- 5+ years of hands-on data science experience shipping production models.
- A 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.
Nice to Have
- Experience with knowledge graphs or ontology modeling.
- Familiarity with enterprise data infrastructure — warehouses, BI tools, semantic layers.
- A background in statistical drift detection or temporal modeling.
- Experience building or evaluating AI agents in enterprise settings.
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