Location: Hybrid (Petach Tikva, Israel) | Experience: 2-3 years | Full-TimeDreamhub is the first AI-native CRM purpose-built for B2B SaaS teams. Backed by the founders of MuleSoft, Datadog, and Datorama, we're redefining how go-to-market teams operate—leveraging machine learning and LLMs to automate workflows, surface deep insights, and eliminate manual data entry.We're looking for a Data Scientist to own production-grade LLM-driven automation that reads customer interactions (emails, call transcripts, meetings) + CRM context and auto-populates CRM fields. You'll ship end-to-end: spec → data readiness → workflow design → evaluation → production integration → continuous improvement.What You'll Do:Drive data readiness and product integration- Take full ownership of field automation projects: from defining field semantics and edge cases to shipping reliable automation in production- Design and implement LLM workflows using our internal framework (from single prompts to multi-step flows with code/tooling) to achieve strong precision/recallDrive data readiness and product integration- Work closely with Product + Engineering to ensure the right data is captured, accessible, and structured for training, evaluation, and runtime inference- Ensure automation flows plug cleanly into our ingestion and data pipelines, so that at the LLM workflow entry point you have the full context you need (emails/transcripts + CRM entities + metadata)- Collaborate on implementation details to make the LLM-flow integration reliable, observable, and easy to maintainEvaluation, quality, and iteration- Build and maintain evaluation sets, metrics, and error taxonomies (precision/recall, coverage, consistency, failure modes)- Run systematic experiments to improve quality: prompt iteration, few-shot strategies, schema-aware prompting, retrieval/re-ranking, and structured output validation- Establish monitoring and feedback loops to detect regressions, measure drift, and improve reliability over timeEngineering-minded execution- Write production-grade Python: clean modular code, reproducible experiments, solid logging, and pragmatic testing- Partner with backend/data infra to ship robust integrations under latency and reliability constraints