Lead - AI and Applied Analytics

AI and Applied Analytics Lead At EY, were all in to shape your future with confidence. Well help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. Job Title: AI and Applied Analytics Lead Experience Level: 10+ Years Designation: Associate Director The opportunity You will lead the development and scaling of AI and analytics products within EYs Global Insights function, equipping EY professionals with differentiated market, sector, and company insights that strengthen EYs brand, deepen client relationships, and drive commercial impact. Working closely with EY Insights leadership, you will help shape our long-term AI and analytics strategy , with a focus on building scalable, enterprise-grade solutions leveraging Azure AI, modern data platforms, MLOps, and generative AI . As the AI and Applied Analytics Lead , you will own a portfolio of AI and analytics products, self-serve tools, and proprietary models. These solutions combine advanced analytics, machine learning, and large language models (LLMs) to deliver foundational intelligence at scale, uncover differentiated insights, and integrate with EYs broader enterprise AI ecosystem. You will also collaborate with the Primary Research Lead to apply AI and advanced analytics techniques in high-impact research programs on critical business topics to uncover novel insights that support publication of thought leadership on EY.com. In this role, you will operate as a playercoach , leading the most complex initiatives while building and mentoring a high-performing, multidisciplinary team of data scientists, data engineers, and visualization specialists. Partnering with analysts, business stakeholders, and technology teams across EY, you will translate strategic priorities into scalable, trusted AI products that leverage enterprise infrastructure, align with governance standards, and integrate cleanly into EYs evolving AI platform. Key Responsibilities AI and Analytics Strategy Shape the long-term AI and analytics strategy for EY Insights in partnership with Global Insights leadership. Identify opportunities to leverage LLMs, AI agents, and agentic workflows to enhance efficiency, insight generation and knowledge discovery. Define how EY Insights products integrate with EYs enterprise AI platforms AI Product Development & Delivery Own delivery and lifecycle management of a portfolio of AI and analytics products, including product roadmap, prioritization, and release cadence. Lead and oversee the development of predictive models, machine learning pipelines, and LLM-enabled applications that generate differentiated insights. Partner with senior analysts and business stakeholders to translate strategic priorities into new, scalable products and solutions. MLOps, Data Platforms & Engineering Excellence Define and implement MLOps standards for model development, deployment, monitoring, and lifecycle management. Ensure AI solutions leverage Azure data platforms, modern data engineering pipelines, and scalable cloud infrastructure. Build strong relationships with technology and governance teams to ensure alignment with enterprise platforms, standards and responsible AI principles. Market Research and Insight development In collaboration with the Primary Research Lead, identify opportunities to apply data science and statistical methods to uncover deeper insights as part of mixed-methods research programs on critical business issues. Advise on appropriate analytical techniques, leading or overseeing modelling and analysis to ensure analysis is rigorous and aligned to research objectives. Support interpretation of findings for a general business audience, helping translate analytical results into insights that support the broader research narrative. Team Leadership & Capability Building Lead and grow the AI and Applied Analytics team, including defining team structure, hiring priorities, and technical mentorship. Operate as a playercoach, contributing hands-on to complex initiatives while guiding delivery across data science, data engineering, and visualization teams. Establish best practices for AI engineering and production delivery, including Git-based workflows, code reviews, CI/CD pipelines, and documentation standards. Foster a culture of innovation, experimentation, and continuous learning Skills and Attributes for Success Deep expertise in AI, machine learning, and applied analytics, with experience delivering production-grade AI solutions. Proven experience building multidisciplinary teams spanning data science, data engineering, and analytics. Strong experience building and scaling AI and analytics products and platforms in enterprise environments. Hands-on experience with MLOps practices, including model lifecycle management, automated pipelines, and monitoring. Experience building or deploying LLM-enabled solutions, such as retrieval-augmented A