Data Scientist

Unleash is now part of Zendesk, and together we’re building intelligent, scalable platforms that bring AI-driven products directly to our customers’ workflows.

We are located in the heart of Tel Aviv, seeking a Data Scientist to join our team.

You will play a key role in developing and deploying the AI capabilities that power our agentic AI experience - from designing retrieval and ranking models to implementing them in production-grade systems.

What You’ll Do

• Build & Deploy: Design, develop, and ship data-driven solutions that directly power our core AI search engine.
• Full-Stack Ownership: Own the entire lifecycle from initial data exploration and modeling to production rollout and real-time monitoring.
• Scale for Growth: Partner with our engineering team to ensure every deployment is scalable, reliable, and performant.
• Optimize Quality: Deep dive into data processing flows to uncover trends, improve output quality, and guide product strategy.
• Iterate & Refine: Constantly push the needle on model efficiency, system stability, and long-term maintainability.
• Collaborate: Work in a tight-knit, high-velocity R&D environment where your ideas turn into code in days, not months.
  
  

• Experience: 3–5 years as a Data Scientist (or similar role), with a proven track record of shipping models to production environments.
• Python Pro: Strong coding skills with a deep understanding of software engineering best practices (clean code, CI/CD, testing).
• DS Foundations: Deep theoretical and practical knowledge of Data Science models from classical machine learning (Trees, Boosting, Regression) to deep learning architectures. You know how to choose the right tool for the job, not just the trendiest one.
• Infrastructure Savvy: Comfortable with the "ops" side of data science hands-on experience with Docker, Kubernetes, and building scalable systems.
• Tooling: Proficient in the standard stack (Pandas, scikit-learn) and modern deep learning frameworks (PyTorch or TensorFlow).
• Cloud Native: Experienced in building and deploying within major cloud ecosystems (AWS, GCP, or Azure).
  
  

• Advanced RAG: Experience building and optimizing RAG (Retrieval-Augmented Generation) pipelines, including advanced retrieval strategies and reranking.
• Agentic Workflows: Familiarity with Agentic RAG architectures (using frameworks like LangChain, LlamaIndex, or CrewAI) to handle complex, multi-step reasoning tasks.
• Vector Infrastructure: Knowledge of vector databases (e.g., Pinecone, Milvus, Weaviate) and semantic search optimization.