Data Science Team Leader

Curious about what it’s like to work at Cognyte?At Cognyte, you’ll collaborate with expert colleagues around the globe to solve problems most people will never even know exist!You’ll be part of building unique solutions shaped by real investigative methodologies, enabling our customers to identify, investigate, visualize and prevent criminal, terror and security threats worldwide.These solutions are used by law enforcement, national security, and national and military intelligence agencies in almost 100 countries to turn massive, diverse data into clear, actionable intelligence for a safer world.We are looking for an exceptional Data Science Team Lead to spearhead the vision and evolution of our next-gen GenAI Co-Pilot. You will lead a team building a game-changing AI product that revolutionizes how users interact with our software - delivering real-time intelligence, deep investigative insights, and autonomous next-action guidance at scale. As a Cognyter, you will: Define the strategic AI roadmap across the project, serving as the core domain authority for cross-functional initiatives. Architect next-gen capabilities by leveraging cutting-edge LLMs, GenAI, and autonomous agentic workflows to help analysts make critical, real-world decisions. Oversee the end-to-end AI/ML lifecycle, from experimentation and prototyping to deployment, monitoring, and optimization. Partner closely with product, engineering, and architecture teams to drive high-quality, scalable solutions. Join a team that fears no technology and constantly tests the boundaries of investigative power.For this mission, you’ll need: An M.Sc. or higher in Computer Science, Mathematics, Engineering, or a related field. 4+ years of hands-on experience building and deploying ML solutions. 2+ years of proven leadership experience managing and growing high-performing data science or ML teams. Proven experience with Large Language Models (LLMs) and Generative AI. Demonstrated expertise in developing and deploying machine learning models and algorithms. Experience in designing ML pipelines, including model versioning, model training & tuning, model deployment, model testing, and monitoring. Experience with model optimizations (quantization, pruning, etc.). Experience with GenAI engineering, implementation and tools, such as LangChain, RAG, Vector Database, transformers, vLLM. Excellent communication skills with the ability to influence, inspire, and align stakeholders.