Sr. Data Scientist

About the role:

Join our fast-growing Global Product Management Data Science team and help transform Gartner’s Client Experience Digital Platform—the essential destination for IT and business leaders worldwide. As a data scientist, you’ll leverage advanced analytics and machine learning to create intelligent, scalable solutions that deliver real value and enhance every step of our clients’ journey.

In this role, you will lead complex data science projects in partnership with cross‑functional teams, driving the development of advanced AI‑powered chatbot systems that deliver intelligent, personalized experiences ’ll architect and implement cutting‑edge conversational AI tools—including intelligent search, recommendation engines, and context‑aware content systems—while ensuring seamless integration with enterprise platforms.

• Lead data science projects in close collaboration with Data Engineering, Application development, Product owners and business leaders to deliver high‑value business capabilities
• Architect and build sophisticated AI‑powered chatbot systems that provide intelligent, personalized client experiences at scale
• Design and implement advanced tools that power conversational AI capabilities, including intelligent search, recommendation engines, and context‑aware content retrieval systems
• Design and implement Model @context Protocol (MCP) servers to enable seamless integration between AI agents, enterprise systems, and external tools
• Build user profiling and personalization models to deliver tailored chatbot experiences
• Be accountable for high‑quality data science solutions with respect to accuracy, coverage, scalability, stability, and business adoption
• Take ownership of algorithms and drive enhancements/optimizations based on business requirements with proper documentation and code‑reusability
• Leverage internal and external data to understand client’s company‑level priorities and deliver targeted support
• Collaborate with senior leadership on long‑term vision, strategy, and solution roadmaps aligned with business objectives
• Pitch ideas, present solutions, and influence senior leaders and executive stakeholders with strong business value propositions
• Stay on top of fast‑moving AI/ML models and technologies, particularly in LLMs, conversational AI, and agentic systems
• Collaborate with engineering and product teams to launch MVPs and iterate quickly
• Independently plan and drive complex data science projects that deliver measurable business value
• Mentor junior data scientists on chatbot development, LLM applications, and best practices

• 6‑8 years hands‑on experience building conversational AI systems, chatbots, LLM applications, or other advanced machine learning/artificial intelligence solutions to drive business impact
• Master’s Degree or PhD in a quantitative field (math, computer science, engineering, etc.) required
• Strong communication skills in technical and business domains with demonstrated ability to translate quantitative analysis into actionable business strategies and influence executive leadership
• Working experience in some of the following data science areas:
  • Large Language Models (LLMs) and Generative AI
  • Conversational AI, chatbot development, and dialogue systems
  • Natural Language Processing and text mining
  • Search and Recommendation systems
  • Prompt engineering, LLM fine‑tuning, and model optimization
  • AI agent architectures and orchestration
• Strong familiarity with Model @context Protocol (MCP) and building tools for AI agents
• Deep understanding of Lean product principles, software development lifecycle, and machine learning life cycle
• Practical, intuitive problem solver with proven ability to translate business objectives into actionable data science tasks and implement state‑of‑the‑art ML research into production systems
• Experience and proficiency with Python, machine learning tools (e.g., scikit‑learn, spacy, nltk), deep learning frameworks (e.g., pytorch, tensor flow, huggingface), LLM frameworks (e.g., Lang Chain, Llama Index), SQL/relational databases (e.g., Oracle), No
  
  SQL databases (e.g., Mongo
  
  DB, graph database), vector databases (e.g., Pinecone, Weaviate),…