Build and enhance AskGartner (Gartners 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:

The ideal candidate will have

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.