Serve as a senior trusted advisor to CIO, CTO, CDO, CRO, business, product, and technology stakeholders. Lead Data & AI discovery and translate ambiguous business problems into measurable, production-ready solutions. Design end-to-end architectures spanning data platforms, analytics, machine learning, GenAI, applications, APIs, governance, and integrations. Lead and challenge advanced forecasting and predictive-modeling approaches, including regression, sparse and zero-inflated data, feature design, model selection, tuning, and validation. Define appropriate business and model success measures, including R², WAPE, MAPE, statistical significance, and business-impact KPIs. Ensure point-in-time correctness, prevent data leakage, and maintain rigorous model-development and validation practices. Provide hands-on technical leadership using Python, SQL, Snowflake, notebooks, Git, and modern Data/AI platforms. Shape AI use cases across forecasting, sponsorship sales, lead scoring, next-best-action, revenue intelligence, personalization, subscription growth, and commercial optimization. Evaluate when classical analytics/ML, GenAI, or agentic AI is the appropriate solution. Lead architecture and solution-design workshops and present recommendations to senior and executive audiences. Support proposals, SOWs, RFI/RFP responses, estimates, staffing models, and technical solution shaping. Lead and mentor multidisciplinary teams across Data Science, Data Engineering, AI Engineering, Software Engineering, and Architecture. Actively participate in client stand-ups, backlog refinement, executive readouts, and strategic planning. Teach and transfer knowledge to client teams; documentation, reproducibility, and handover are expected parts of delivery. Challenge client requests when the proposed approach does not solve the underlying business problem.