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 - valuable 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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