Sr Data Scientist

Gartner•Stamford, CT

2d•$113,000 - $147,000•Hybrid

About The Position

Join our dynamic BTI Data Science team and help transform Gartner’s research content & insights operation. In partnership with our PMO and IT organizations, you will help build the AI applications and systems that empower our Expert Analysts to perform their work with unprecedented efficiency and depth. As a Senior Data Scientist, you will lead complex AI and data science projects in partnership with cross-functional teams and their leaders, steering the development of advanced agent systems and agentic workflows to create intelligent, scalable solutions that deliver tangible value and enhance every step of the Analyst’s journey. You’ll architect and implement cutting-edge agentic AI solutions while ensuring seamless integration with enterprise platforms.

Requirements

6-8 years of hands-on experience in advanced ML engineering and enterprise tool/product development, including at least 2 years deploying and managing LLMs in production, and over 1 year designing and implementing agents and multi-agent systems for enterprise business applications.
Bachelor’s degree required; Master’s or PhD preferred.
Strong communication skills in technical and business domains with demonstrated ability to translate ML/AI technology solutions into actionable business strategies and influence executive leadership
Experience and proficiency with Python, deep learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face), LLM & Agentic frameworks (e.g., LangChain, LangGraph, LlamaIndex), SQL/relational databases (e.g., Oracle), NoSQL databases (e.g., MongoDB, graph database), vector databases (e.g., Pinecone, Weaviate), distributed machine learning (Spark), AI evals and observability solutions
Working experience in some of the following AI and data science areas: Large Language Models (LLMs) and Agentic AI Prompt Engineering, Context Engineering, Harness Engineering
Conversational AI, chatbot development, and dialogue systems
Natural Language Processing and text mining
Search and Recommendation systems
LLM fine-tuning, and model optimization
AI agent architectures, orchestration, and observability
Strong familiarity with Model Context Protocol (MCP) and building tools for AI agents
Strong understanding of Lean product principles, software development lifecycle, and ML/AI life cycle
Proven ability to translate business objectives into actionable AI and data science tasks and implement state-of-the-art ML/AI research into production systems
Experience with cloud computing services such as AWS or Azure ML
Strong ability to work collaboratively across product, data science and technical stakeholders with experience mentoring data scientists
Ability to work in a culture that thrives on feedback and seeks opportunities to stretch outside comfort zone
Bias for action and user and customer-outcome oriented

Nice To Haves

While degrees in mathematics, computer science, engineering, or other quantitative fields are advantageous, candidates with a strong academic background in the humanities or other fields who also have relevant experience in quantitative methods, natural language processing (NLP), or artificial intelligence (AI) are encouraged to apply.

Responsibilities

Lead data science projects in close collaboration with IT, Data Engineering, Application development, PMO and business leaders to deliver high-value business capabilities
Architect and build sophisticated agent systems and agentic workflows that provide intelligent, personalized Analyst experiences at scale
Design and implement advanced AI tools that facilitate human-in-the-loop content planning, content production, and insights creation, prioritizing our Analysts’ expertise
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 AI and 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 Analysts’ priorities and deliver targeted support
Collaborate with leadership on long-term vision, strategy, and solution roadmaps aligned with business objectives
Pitch ideas, present solutions, and influence senior leaders and stakeholders with strong business value propositions
Stay on top of fast-moving AI/ML models and technologies, particularly related to LLMs, multi-agent systems, agentic workflows, agentic RAG, deep agents, emerging AI architectures, and emerging generative UI/UX solutions
Collaborate with engineering and product teams to launch MVPs, iterate quickly, and drive solutions toward production
Independently plan and drive complex data science projects that deliver measurable business value (and measure that value/ROI)
Mentor junior data scientists in AI Engineering, LLM app development, and best practices