Data Science Team Lead

GigaSpaces is seeking an experienced Data Science Team Leader to guide a team of 3 Data Scientists in the rapidly evolving Retrieval-Augmented Generation (RAG) domain. In this role, you will mentor the team, drive innovation in RAG solutions, and enhance the company's expertise in generative models and reasoning techniques. Your team will focus on improving the accuracy and efficiency of RAG systems by integrating retrieval and generation methods for smarter, context-aware outputs.

You will collaborate with cross-functional teams to apply RAG techniques to both structured and unstructured data, shaping the strategic direction for RAG advancements within the company.

This full-time, hybrid role is located in Herzeliya and reports to the VP of R&D.

Key Responsibilities:

Team Leadership & Mentorship: Lead and mentor a team of 3 data scientists in the RAG domain, fostering collaboration, innovation, and continuous learning. Provide technical guidance and career development support, encouraging a culture of experimentation and knowledge-sharing.
Research & Innovation: Drive innovation in RAG accuracy techniques and performance. Lead the development of cutting-edge methods in GenAI, reasoning, embedded models, generative models, and retrieval techniques. Stay updated on trends to improve RAG workflows.
Model Development & Optimization: Oversee the design and deployment of advanced RAG models for structured and unstructured data. Enhance retrieval and generation systems using embedding models, search algorithms, and neural retrieval techniques. Implement novel evaluation metrics for accuracy and relevancy.
Cross-Functional Collaboration: Collaborate with engineering, product, and business teams to align RAG solutions with company needs. Translate business requirements into effective data science strategies.
Scalability & Productionalization: Ensure scalability and robustness of RAG solutions, optimizing deployment and performance in collaboration with infrastructure teams. Lead automation efforts for streamlined RAG workflows.
Thought Leadership & Industry Engagement: Represent the company at conferences, webinars, and meetups, sharing progress in RAG. Contribute to research papers, blogs, and open-source projects to establish the company as an industry leader in AI and RAG.

Requirements:

Required Skills and Experience:

Education: Advanced degree (MS or PhD) in Computer Science, Data Science, Artificial Intelligence, or a related field.
Experience: At least 7 years of experience in data science, with a strong focus on RAG, generative models, or NLP (Natural Language Processing).
Proven experience in leading and mentoring a team of data scientists or engineers, driving high-performance results.
Strong background in GenAI fundamentals, reasoning, embedded models, generative models, and both structured and unstructured data processing.
Expertise in Retrieval-Augmented Generation (RAG) techniques, with hands-on experience building and deploying RAG systems in production environments.
Deep understanding of machine learning frameworks (TensorFlow, PyTorch, etc.) and experience with state-of-the-art LLMs (GPT, BERT, T5, etc.).
Familiarity with relevant technologies for retrieval (e.g., vector search, dense retrieval, etc.) and integration with generative models.
Experience with evaluating model performance, developing metrics, and optimizing models for high accuracy and efficiency.
Strong programming skills in Python, along with experience using libraries such as Hugging Face, OpenAI, or similar for generative models and NLP.
Familiarity with cloud platforms (AWS, GCP, Azure) and ML operations (MLOps) for deploying and maintaining models at scale.

Preferred Skills and Experience:

Experience in embedding techniques (e.g., Sentence-BERT, FAISS) for semantic search and retrieval tasks.
Knowledge of reinforcement learning and other advanced techniques for improving reasoning and decision-making in generative AI systems.
Experience in managing large-scale data pipelines and working with big data technologies (e.g., Spark, Hadoop).
Publications in top-tier AI/ML conferences or journals are a plus.
Familiarity with AI ethics and bias mitigation techniques in generative models.