R&I Team Lead

Position Overview

We are looking for a hands-on R&I Team Lead to build and drive our next-generation AI capabilities across anomaly detection, explainable AI (XAI), agent-based systems, and state modeling. This is not a coordination role. You will lead from the front: shaping research directions, writing code, validating ideas in production environments, and turning ambiguity into working systems. You will manage a small, high-impact team in Herzliya while working closely with platform, product, and domain experts to ensure research translates into real-world value.

• Lead applied research end-to-end:
  From idea to production: problem framing, experimentation, modeling, evaluation, and deployment.
• Be hands-on:
  Design and implement ML/DL/LLM-based solutions yourself. Set the technical bar by example.
• Define and execute R&I roadmap:
  Focus areas include anomaly detection, state estimation, hybrid modeling, and explainable AI (XAI), including causal inference and counterfactual reasoning.
• Bridge research and product:
  Translate open-ended questions into structured experiments and production-ready components.
• Build and mentor a strong team:
  Grow a multidisciplinary team (data scientists, analysts, AI engineers) with high ownership.
• Drive technical excellence:
  Establish best practices for experimentation, validation, and reproducibility.
• Collaborate across the organization:
  Work closely with platform teams and domain experts to ensure alignment and impact.
• Coordinate with external scientific advisors:
  Work effectively with domain consultants (e.g. physicists, probabilistic modeling experts) to integrate their insights into the team’s research agenda.
• Communicate clearly and effectively:
  Present complex ideas to both technical and non-technical stakeholders. This role sits at the intersection of research, engineering, and business.

• 10+ years experience
• 5+ years of hands-on experience in Machine Learning / Deep Learning / Applied AI
• Proven experience leading technical projects or small teams
• Strong coding skills in Python and modern ML/DL frameworks (PyTorch / Tensor Flow)
• Experience with real-world, messy data (time-series, sensor data, industrial systems is a big plus)
• Solid understanding of experimentation, model evaluation, and statistical reasoning
• Experience taking models from research to production
• Strong system thinking. You don’t just build models, you build solutions

• Background in industrial, sensor-based, or time-series prediction problems.
• Familiarity with anomaly detection and root cause analysis.
• Comfort working in fast-paced environments with evolving data and priorities