Product Manager - Artificial Intelligence

Job Description

The IBM Z organization is looking for an AI on Z Product Manager to work with topics including open source packages, Linux on Z strategy, and technical enablement for business scale out of AI. An ideal candidate would be experienced with AI concepts, familiar with Linux on Z, and comfortable in technology discussions. This candidate should be comfortable learning & teaching new skills, a good listener for client and sales discussions, and be able to address analytical business questions with research and interactions with both development and clients.This role will include some investigation into country or industry-based regulatory requirements coming in the future related to AI model prediction accuracy, governance and drift over time. Required education Master's Degree Preferred education Master's Degree Required technical and professional expertise 5+ years of either Product Management or Product Development experience, with responsibility for researching feature value, developing features, and managing the lifecycle of offerings, preferably on IBM Z or IBM LinuxONE. Expertise in Analytics, including defining and measuring key performance indicators (KPIs). AI experience to build from – The experience could be a wide range of scenarios, as a Db2 SQL user, analytics professional, a machine learning or AI advocate, etc. Good People and Project Management skills, as this person will need to adjust quickly to get best results from unique or unexpected scenarios. Since the AI space is new, we expect to be iterating products with sponsor users, knowing that some products will need to evolve from their initial MVP. Candidates should be good communicators in order to keep teams in sync across the various business milestones.ng manager and Recruiter should collaborate to create the relevant verbiage. Candidates should have exposure to industry and country based regulations and regulatory bodies. The candidate should be comfortable documenting model performance findings, as well as evaluating potential risks of model inaccuracies.