Lead Data Scientist

Job Summary

Responsibilities

• Build and enhance AskGartner (Gartner s AI Advisor) with insights and capabilities from Gartner events and ensure seamless user experience between Events and Gartner.com digital platforms
• Build in-house Agentic products fine tune LLMs to improve AskGartner and Gartner conferences Agentic products
• Build optimize Recommender systems for Gartner Global Conferences. These models ensure improved user experience before, during and after a conference
• Build Roll-out ML/NLP/Deep learning models to improve Attendees/Exhibitor acquisition retention
• Work closely with business to refine requirements around conferences business, build quick POCs, present findings in a consumable report or streamlit app
• Enrich conferences client dataset by leveraging scraping, predictive and generative ai tooling capabilities
• Improve upon MLOps capabilities by building and optimizing data pipelines, model monitoring and training capabilities
• Build Evaluation Frameworks to ensure products development are tied to metrics that move the needle and can be measured offline
• Automated solutions that help conferences business and strategy teams with analytics and insights capabilities for efficient decision making
• Work closely with business and engineering counterparts to develop deploy solutions involving production grade micro-services, data pipelines, insight dashboards

Qualifications

• 8+ years of hands-on experience in data science alongside a degree in computer science, statistics, mathematics or a related field. Masters or PHD highly preferred
• Hands on experience with GenAI dev tools and frameworks like LangGraph/Langchain, Langfuse
• Experience building recommender systems, search engine query pipelines for end user applications
• Experience building and deploying enterprise grade machine learning/NLP/Deep learning, AI Agents in close collaboration with engineering and product managers
• Experience leveraging and fine-tuning Large Language Models (LLMs) or building/distilling SLMs is a plus
• Solid programming skills in Python, data science libraries (scikit-learn, TensorFlow/PyTorch, HuggingFace, etc.) and GenAI tools like claude-code, copilot
• Experience building and deploying machine learning models in close collaboration with engineering - good understanding of CI/CD pipelines
• Hands-on experience with big data technologies (PySpark) and familiarity with cloud platforms (AWS, GCP, Azure) to explore insights and working with complex datasets
• Strong ability to communicate technical concepts to non-technical audiences

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