Lead Data Scientist

About the Role

We are seeking a visionary and hands-on AI and Data Science Team Lead to head our advanced analytics and AI engineering group. Over the past 7 years, our data infrastructure and customer needs have evolved from foundational data engineering and cloud migrations to cutting-edge Generative AI and Machine Learning solutions.

You will lead a high-performing team of AI Engineers and Data Scientists, taking ownership of the technical direction, architecture, and deployment of production-grade AI systems on AWS. The ideal candidate combines a solid foundational background in complex data architecture with state-of-the-art expertise in Gen AI, RAG pipelines, graph-based reasoning, and Enterprise MLOps. You will work closely with enterprise customers across various sectors (manufacturing, banking, healthcare, and high-tech) to translate business constraints into scalable architectures and ship systems that deliver measurable business impact.

Responsibilities

• Team Leadership: Recruit, mentor, and manage a team of Data Scientists and AI Engineers, driving engineering excellence, technical direction, and best practices.
• System Architecture & Gen AI: Design and deploy end-to-end AI systems, including large-scale Retrieval-Augmented Generation (RAG) pipelines for technical documentation, Text-to-SQL GenBI agents, and advanced AI document processing systems (OCR, semantic extraction).
• Enterprise MLOps: Design and deliver secure, enterprise-grade MLOps platforms on AWS (including CI/CD pipelines, feature stores, and monitoring) to dramatically reduce model deployment times.
• Advanced Data Modeling: Utilize graph embeddings (e.g., Neo4j) and build graph-based AI agents to uncover hidden behavioral patterns, improving LLM reasoning while optimizing token costs and latency.
• Customer & Stakeholder Collaboration: Work directly with enterprise customers to understand business constraints, define architectural standards, and implement governance guardrails and validation layers for safe enterprise adoption and data protection.
• Hands-On Execution: Operate dynamically in both startup and enterprise contexts, balancing the need to ship value quickly with maintaining strict production rigor.

Requirements

• Experience: 7+ years of proven experience designing, deploying, and managing production-grade Machine Learning and AI systems (with a strong foundation in data engineering and scalable pipelines).
• Leadership: Demonstrated experience leading, managing, and mentoring data science or AI engineering teams.
• Cloud & Infrastructure: Deep architectural expertise in the Amazon Web Services (AWS) ecosystem, with a track record of deploying large-scale solutions. Experience defining architectural standards to achieve milestones like the AWS ML Competency is a significant advantage.
• Gen AI & Search: Extensive hands-on experience with Large Language Models (LLMs), deep learning frameworks, domain-specific prompting, and vector search/Elasticsearch.
• Graph Technologies: Strong background in modeling complex data structures using Graph Databases (e.g., Neo4j) to build scalable pipelines for high-volume data.
• Education: M.S. (or equivalent) in Engineering, Mathematics, Computer Science, or a related quantitative field.
• Communication: Excellent interpersonal skills with the ability to lead customer-facing engagements and translate complex technical concepts into business value.
• Fluent in Hebrew and English