Join NTT DATA as a Data & AI Consulting Manager and drive transformative AI initiatives for our clients. This client-facing role within our AI Go-to-Market organization is crucial for identifying, shaping, and executing AI opportunities. You'll leverage your strategic acumen, AI fluency, and consulting expertise to help clients define AI value propositions, operating models, and roadmaps. Collaborate with AI Architects, delivery teams, and partners to turn client needs into successful engagements and drive measurable enterprise value.

As an AI Consultant, you will act as a trusted advisor, identifying AI transformation opportunities and business challenges. You will shape AI strategies, including target operating models, governance, adoption plans, and responsible AI frameworks. Translate complex client needs into actionable AI solutions in partnership with technical teams. Develop compelling proposals, business cases, ROI models, and executive-ready client materials. Support presales activities from opportunity qualification through mobilization and transition to delivery. Lead structured consulting activities like workshops and assessments. Maintain up-to-date knowledge of AI trends, agentic AI, responsible AI, and partner capabilities to enhance client advisory.

We are seeking professionals with a minimum of 8 years of experience in consulting, professional services, strategy, transformation, AI, data, or related client-facing roles. A strong background of at least 3 years in shaping or delivering AI, data science, machine learning, GenAI, agentic AI, analytics, or digital transformation initiatives is essential. Demonstrated strategic thinking abilities, including business model analysis, operating model design, and value case development, are required. Experience with presales activities such as opportunity identification and solution shaping is also necessary. A Bachelor's degree or equivalent work experience is a prerequisite, along with a solid understanding of enterprise AI concepts like LLMs, AI agents, RAG, ML lifecycles, and responsible AI.