\\\\Introduction\\\\ At IBM, we believe technology shapes the world. Were a catalyst for that innovation. Were driving change that improves businesses, society, and the human experience. Our Marketing, Communications & Corporate Social Responsibility (MCC) team tells this story. We shape IBMs brand, capture attention in the market, and share our perspective with clients, partners, the media, and fellow IBMers. On our team, youll work with bright, collaborative minds who bring passion and creativity to everything they do. Youll be part of a culture built on openness, trust, and teamwork. Where your ideas matter and your growth is supported. Join us and help bring innovation to life. \\\\Your role and responsibilities\\\\ We are building a small, elite AI strike team embedded in the Marketing, Communications, and CSR (MCC) Strategy & Operations team. The mission is to move fast, explore whats possible with agentic and conversational AI, and prove value quicklybefore anything is scaled or productionized elsewhere. This team operates as a rapid-action unit focused on experimentation, learning, and proof-of-value rather than long-term platforms or production systems. In this role, you help leaders see value clearly and early. You translate imperfect data and early AI experiments into insight that accelerates go / no-go decisions. THIS ROLE IS Focused on insight, clarity, and decision support Embedded in a small AI experimentation team Optimized for speed and iteration Closely partnered with AI Engineers and stakeholders THIS ROLE IS NOT A traditional dashboard or reporting backlog role A long-term analytics ownership position A production BI function KEY RESPONSIBILITIES Translate vague questions into structured analyses Create lightweight visuals and analytical artifacts for AI POCs Evaluate whether AI experiments are delivering value Identify opportunities, risks, and tradeoffs in imperfect data Support clear storytelling for senior decision-makers Adapt analyses quickly as hypotheses evolve \\\\Required technical and professional expertise\\\\ Strong experience in data analysis and visualization Solid SQL skills and modern analytics tool experience and experience in collecting, manipulating, and analyzing large data sets to identify trends using standard methods, including scripting and coding in R, C++, and/or Python. Proven ability to work with incomplete or evolving data Strong storytelling and communication skills Experience to effectively conveying data analysis results to technical stakeholders, such as Data Scientists, and influencing how data is organized, stored, and cleaned. Comfort operating in ambiguity \\\\Preferred technical and professional experience\\\\ Experience supporting experiments or pilots Familiarity with AI-adjacent analytics Exposure to marketing or operational datasets Exposure to scripting and coding in multiple languages, including R, C++, and/or Python, to extract insights from large data sets. Experience working with data storage solutions, ensuring compliance with architectural standards for data management resources and infrastructure. IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.

The role requires strong experience in data analysis and visualization, along with solid SQL skills and familiarity with modern analytics tools. Candidates must have experience collecting, manipulating, and analyzing large datasets to identify trends using standard methods, including scripting and coding in R, C++, and/or Python. A proven ability to work with incomplete or evolving data is essential, as are strong storytelling and communication skills to effectively convey analysis results to technical stakeholders and influence data organization. Comfort operating in ambiguity is also necessary.