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’  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.

What you will doLead 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

What you will need6-8 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., MongoDB, graph database), vector databases (e.g., Pinecone, Weaviate), distributed…