Data Science Team Lead

Description

GigaSpaces is seeking an experienced Data Science Team Leader to lead a dynamic team of 3 Data Scientists working in the exciting and rapidly evolving domain of Retrieval-Augmented Generation (RAG). In this leadership role, you will be responsible for guiding and mentoring your team, driving innovation in RAG-based solutions, and advancing the company's expertise in both generative models and reasoning techniques. Your team will work across a variety of cutting-edge technologies, focusing on improving the accuracy and efficiency of RAG systems by integrating retrieval and generation methods for more intelligent, context-aware outputs.

As the team leader, you will work closely with cross-functional teams to ensure the successful application of RAG techniques to both structured and unstructured data, and you will play a critical role in setting the strategic direction for RAG advancements within the company.

Reporting to our VP R&D, this position is full time, hybrid and located in Herzeliya.

Key Responsibilities:

Team Leadership & Mentorship:

Lead and mentor a team of 3 data scientists in the RAG domain, fostering an environment of collaboration, innovation, and continuous learning.
Provide technical guidance and career development support to team members, helping them grow in both skills and leadership capabilities.
Promote a culture of experimentation and knowledge-sharing to stay at the forefront of RAG research and development.
Adopt a Fail Fast approach for rapid iterations of research2.

Research & Innovation:

Lead the company's efforts to innovate in RAG accuracy techniques and improve the performance of retrieval-augmented systems.
Drive the development and implementation of cutting-edge techniques in GenAI fundamentals, reasoning processes, embedded models, generative models, and retrieval methods.
Stay updated with the latest trends in generative AI, retrieval-augmented techniques, and large language models (LLMs), applying new ideas and approaches to improve RAG workflows and outcomes.

Model Development & Optimization:

Oversee the design, development, and deployment of advanced RAG models that work seamlessly with both structured (e.g., databases, tabular data) and unstructured data (e.g., text, images, documents).
Utilize your expertise in embedding models, search algorithms, and neural retrieval techniques to enhance the accuracy and efficiency of information retrieval and generation systems.
Lead the implementation of novel evaluation metrics and benchmarks to assess and improve the accuracy, relevancy, and explainability of RAG systems.

Cross-Functional Collaboration:

Collaborate with engineering, product, and business teams to understand the needs and requirements for RAG applications across different company initiatives.
Translate business requirements into effective data science solutions, ensuring alignment with strategic goals and customer needs.

Scalability & Productionalization:

Ensure the scalability and robustness of RAG solutions for real-world applications, collaborating closely with infrastructure teams to optimize deployment and performance.
Lead efforts to automate and streamline RAG workflows, from model training and tuning to serving and inference.

Thought Leadership & Industry Engagement:

Represent the company at industry conferences, webinars, and meetups, sharing your team's progress in RAG and related areas.
Contribute to research papers, blog posts, and open-source projects to position the company as a thought leader in the AI and RAG domains.
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.