Associate Director - Applied AI and Analytics
EY · Gurugram
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AI, Analytics & Insights Transformation Lead
Associate Director | EY Global Insights
Experience Level 15+ Years
Designation
Associate Director
The opportunity
You will lead the transformation, development and scaling of AI and analytics products within EY’s Global Insights function, equipping EY professionals with differentiated market, sector, company and thematic insights that strengthen EY’s brand, deepen client relationships and drive commercial impact.
Working closely with Global Insights leadership, you will shape and execute a multi-year AI and analytics transformation strategy. The role sits at the intersection of business strategy, research, insights, analytics, product innovation and emerging technology, with a focus on converting AI ambition into trusted, scalable and adopted enterprise capabilities.
As the AI, Analytics & Insights Transformation Lead, you will own a portfolio of AI and analytics products, decision-support capabilities, self-service tools and proprietary models. These solutions may combine advanced analytics, machine learning, large language models (LLMs), AI agents, knowledge systems and modern data platforms to deliver differentiated intelligence at scale and integrate with EY’s broader enterprise AI ecosystem.
You will also collaborate with the Primary Research Lead, sector specialists, analysts and subject-matter experts to apply AI and advanced analytical methods in high-impact research programs, uncovering novel insights that support thought leadership and decision-making.
In this role, you will operate as a senior player-coach: setting direction and leading the most complex initiatives while building and mentoring a high-performing multidisciplinary team spanning AI product management, data science, data engineering, analytics and visualization. You will partner across Insights, Markets, Technology, Risk and other enterprise functions to translate strategic priorities into scalable, governed products and new ways of working.
Key Responsibilities
AI and Analytics Strategy
- Shape and communicate the long-term AI and analytics strategy for EY Global Insights in partnership with senior leadership, connecting emerging capabilities to insight quality, differentiation, productivity, adoption and commercial impact.
- Identify opportunities to leverage generative AI, LLMs, AI agents, agentic workflows, advanced analytics and decision intelligence across research, knowledge discovery, insight generation and decision support.
- Define where AI should automate work, where it should augment human judgment and where human-led expertise should remain central.
- Develop a prioritized transformation roadmap and target operating model that aligns people, process, data, technology, governance and adoption requirements.
- Define how Global Insights products and capabilities integrate with EY’s enterprise AI, data and knowledge platforms, and represent the transformation agenda in senior leadership and governance forums.
AI Product Development & Delivery
- Own delivery and lifecycle management of a portfolio of AI and analytics products, including product strategy, roadmap, prioritization, investment choices, release cadence, adoption and value realization.
- Lead development of AI-powered capabilities across areas such as account intelligence, sector intelligence, market sensing, competitive intelligence, research synthesis, thought leadership and decision support.
- Ensure product development begins with a clearly articulated business problem, user need and value hypothesis rather than a technology-first solution.
- Evaluate the appropriate use of LLMs, retrieval-augmented generation, knowledge graphs, AI agents, predictive analytics and other techniques, including build, buy, partner and enterprise-platform options.
- Establish portfolio governance and performance measures to support evidence-based decisions on scaling, redesigning, integrating or retiring capabilities.
MLOps, Data Platforms & Engineering Excellence
- Define production and lifecycle standards for AI and analytics products, including development, validation, deployment, monitoring, observability, documentation and continuous improvement.
- Ensure solutions leverage modern enterprise cloud and data platforms, with strategic oversight of Microsoft Azure, Azure AI services, Azure Machine Learning, Azure OpenAI Service, Databricks and related technologies.
- Partner with enterprise architecture, data, cybersecurity, privacy, legal, risk and responsible AI teams to ensure solutions are scalable, interoperable, resilient, secure and aligned with EY standards.
- Establish principles for data sourcing, provenance, quality, lineage, licensing, access, traceability and responsible use, including appropriate human review for AI-generated outputs.
- Promote strong engineering disciplines including Git-based collaboration, code review, CI/CD, infrastructure automation and reusable delivery patterns, while ensuring technology choices remain subordinate to business and product outcomes.
Market Research and Insight Development
- In collaboration with the Primary Research Lead, identify opportunities to apply AI, data science and statistical methods to mixed-methods research programs on critical business and market issues.
- Advise on appropriate analytical techniques, including statistical analysis, machine learning, network analysis, natural language processing, simulation and related methods, ensuring the approach is aligned to the research question.
- Lead or oversee modelling and analysis so that methods are rigorous, transparent, explainable and supported by appropriate quality controls, source validation and traceability.
- Support interpretation of complex findings for general business and executive audiences, connecting analytical evidence to strategic narratives and decision-making implications.
- Identify opportunities to create differentiated intellectual property and new forms of insight, including proprietary indices, market signals, benchmarks, interactive research experiences and decision-support tools.
Team Leadership & Capability Building
- Build and lead a high-performing multidisciplinary AI and Applied Analytics team, defining team structure, capability requirements, hiring priorities, sourcing strategy and technical leadership needs.
- Operate as a player-coach, contributing directly to the most complex initiatives while guiding delivery across AI product management, data science, data engineering, analytics and visualization.
- Develop senior team members who can independently lead products, transformation workstreams and stakeholder relationships, and establish communities of practice and reusable capability frameworks.
- Lead change management and adoption efforts so AI-enabled products and workflows become embedded in real research and insight-development processes rather than remaining standalone tools or pilots.
- Foster a culture of innovation, experimentation, accountability, intellectual rigor and continuous learning, with teams measured on user value and outcomes as well as technical delivery.
Skills and Attributes for Success
- 15+ years of professional experience, with substantial leadership experience across AI, analytics, digital transformation, insights, strategy, product or technology-enabled business change.
- Proven experience leading complex, enterprise-scale transformation programs across multiple functions, geographies or business groups, with the ability to convert strategic vision into an executable roadmap and operating model.
- Strong understanding of AI, machine learning, generative AI and applied analytics, with experience building and scaling production-grade AI, analytics, insight or decision-support products in enterprise environments.
- Experience leading portfolios with responsibility for prioritization, investment, governance, adoption, lifecycle management and measurable value realization.
- Strong understanding of LLM-enabled solutions, retrieval-augmented generation, AI agents, agentic workflows, enterprise knowledge systems and human-in-the-loop models.
- Strong understanding of modern cloud AI and data ecosystems, particularly Microsoft Azure, Azure AI services, Azure Machine Learning, Azure OpenAI Service, Databricks and scalable data architectures.
- Working knowledge of MLOps, LLMOps, DevOps, CI/CD, model monitoring, observability, Git-based development workflows and production lifecycle management; the role does not require the candidate to be the principal engineer.
- Strong expertise in predictive modelling, statistical analysis and machine learning methods, with the judgment to select techniques appropriate to the research or business question.
- Experience working with researchers, analysts, economists, sector specialists or thought-leadership teams, and an understanding of quantitative and qualitative research methods, is highly desirable.
- Exceptional executive communication, storytelling and stakeholder-management skills, with the ability to explain complex technical concepts in clear business language, build investment cases and influence senior global stakeholders.
- Strong understanding of responsible AI, privacy, intellectual property, data governance, provenance, licensing, security, human oversight, transparency and explainability in enterprise delivery.
What We Look For
- A senior transformation leader who thinks beyond models and technologies to business outcomes, organizational change, sustained adoption and measurable value.
- A strong ownership mindset with demonstrated ability to lead complex initiatives from strategy and experimentation through production, scale and value realization.
- The ability to articulate a compelling and credible vision for AI-powered insights and translate that vision into prioritized products, operating models and investment choices.
- Executive presence and strategic influence, with the confidence to engage, challenge and align senior stakeholders across a complex, globally distributed organization.
- A strong understanding of the intersection of research, insights, analytics, products, technology and commercial decision-making, including the judgment to identify where AI creates genuine differentiation and where conventional analytics, process redesign or human expertise may be more appropriate.
- A track record of building multidisciplinary teams and sustainable delivery capabilities, while combining intellectual curiosity and innovation with commercial discipline, governance and accountability.
What We Offer
- The opportunity to define and lead the AI-enabled transformation of EY’s Global Insights capability.
- Direct exposure to senior global leadership and the ability to influence high-impact strategic priorities and insight products with commercial relevance.
- The mandate to develop differentiated AI and analytics products across research, market intelligence, account intelligence, sector insights and thought leadership.
- Access to modern enterprise data, AI, cloud, research and technology platforms, with the opportunity to shape how they are used and scaled within Global Insights.
- The opportunity to build and lead a multidisciplinary team spanning product, AI, analytics, data engineering and visualization.
- A collaborative global environment focused on innovation, trust, intellectual rigor, experimentation and measurable impact.
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