Job Description
Red Hat's Global Sales Go-To-Market (GTM) team is looking for a high-impact Principal Data Scientist to bridge the gap between complex data architecture and executive strategy. This isn't a "maintenance" role-you will be an architect of change, leveraging AI, advanced statistics, and a builder's mindset to reinvent how we handle quotas, incentives, and sales capacity. We need a hybrid talent: someone with the technical depth to code automated pipelines from scratch and the business savvy to translate those results into a "digestible story" for executive leadership. What You Will Do Architect from Scratch: Design and deploy end-to-end automated processes for the quota and incentive lifecycle, ensuring bulletproof data validation Scale AI Integration: Use advanced prompt engineering and LLM tools to accelerate development, audit code, and eliminate manual workflows Master the Pipeline: Extract and enrich massive datasets from Snowflake and GitHub, ensuring data integrity across complex logic layers Tell the Story: Translate highly technical outputs into strategic recommendations. You will present key performance metrics directly to executive leadership Solve the "Puzzle": Apply predictive modeling and pattern recognition to evaluate sales coverage and optimize customer engagement at scale What You Will Bring Programming: Mastery of Python (Pandas, Google Cloud libraries) and SQL (Snowflake/DBeaver) AI Fluency: You treat AI as a primary collaborator, using prompt engineering to increase your velocity Statistics: Deep knowledge of regression, simulation, scenario analysis, clustering, and decision trees Data Ops: Experience with GitHub workflows and building "self-healing" scripts to prevent recurring data errors Visualization: Ability to build impactfu