Advanced Analytics and AI Lead

We need to output HTML fragment with sections only if real content. Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must not repeat job title. No Job ID etc. Remove boilerplate. Keep substantive info. We need to parse posting: It starts with intro about EY, then "The opportunity" description. That could be "About the role". Then sections: Essential Functions (list of duties) -> Responsibilities. Then sub-sections: Talent Intelligence Strategy & Executive Advisory, Advanced Analytics & AI Solutions, Generative AI, Copilot & Agentic Solutions, Data Products & Platform Leadership, Insights, Storytelling & Executive Communications, Leadership & Capability Development, Analytical/Decision Making Responsibilities. Those are responsibilities. Then Knowledge and Skills Requirements: sub-sections: Strategic & Business Skills, Analytics & Data Science Skills, AI & Emerging Technology Skills, Data & Technology Skills, Leadership Competencies. Those could be Skills or Requirements? The instruction: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we need to map content accordingly. We have "Job Requirements: Education: ..." that's Qualifications. Also Certification Requirements, Experience. We have "Other Requirements: Due to global nature..." that's maybe Requirements. We have "What We Offer You (Canada)" which is Benefits. No explicit Pay or Schedule mentioned. So we should not include Pay or Schedule sections. We need to include About the role (intro). Responsibilities (essential functions and subfunctions). Requirements (maybe combine Job Requirements and Other Requirements). Skills (Knowledge and Skills Requirements). Benefits (What We Offer). Possibly Qualifications separate? But we have both Requirements and Qualifications headings allowed. Could put Education/Certification/Experience under Qualifications. And Other Requirements under Requirements (travel, language). Let's decide. We need to preserve all substantive info, not invent. We'll output HTML with sections:

About the role

... intro paragraph(s) ...

Responsibilities

• ... each essential function bullet ...
...

We need to break down responsibilities into list items. The posting has many paragraphs; we can combine into list items each representing a major responsibility area. We must not invent or summarize away detail; we need to preserve all substantive information. That means we should not omit any detail. But we can group similar items? The instruction: "Preserve ALL substantive information; do NOT invent or summarize away detail." So we need to keep each piece of info. If we combine multiple sentences into one list item, we might be summarizing? It's okay if we keep the content but not omit. Summarizing away detail means we shouldn't drop details. Combining into a single bullet that contains all details is okay as long as we don't lose any detail. However we need to be careful not to lose nuance. Safer to create separate list items for each distinct responsibility area, but ensure we keep all details. Let's extract responsibilities sections: Essential Functions: Talent Intelligence Strategy & Executive Advisory - Partner with Global Talent Leadership, Function Talent Leaders, Service Line Leaders, and Planning leaders to define and execute the Talent Intelligence strategy. - Act as a trusted advisor to executive stakeholders, translating complex workforce trends into strategic recommendations and actionable decisions. - Drive data-informed decision making across planning, skills transformation, workforce evolution, talent acquisition, retention, learning, talent mobility, and succession planning, etc - Deliver executive-ready narratives that combine quantitative insights with business context and future workforce implications. - Influence enterprise talent priorities through predictive and prescriptive analytics. Advanced Analytics & AI Solutions - Lead the development of advanced analytics models to identify workforce risks, forecast talent demand, predict workforce trends, and uncover opportunities to improve organizational performance. - Design and deploy AI-powered talent intelligence solutions using machine learning, Generative AI, natural language processing, knowledge graphs, and predictive modelling techniques. - Establish scalable approaches for skills intelligence, workforce segmentation, talent forecasting, organizational network analysis, and workforce optimization. - Define methodologies that combine internal talent data with external labour market intelligence and economic indicators. - Continuously evaluate emerging technologies and analytics techniques to advance talent decision science capabilities. Generative AI, Copilot & Agentic Solutions - Lead development of AI-enabled experiences using Data Bricks, Microsoft Fabric, Power BI, Microsoft Copilot, Copilot Studio, Azure AI, enterprise GenAI platforms and machine learning, and intelligent automation capabilities. - Design and implement intelligent agents that automate talent insights generation, workforce analytics, executive insights, and decision support processes. - Build conversational analytics experiences that enable leaders to interact with talent data through natural language. - Establish governance, explainability, responsible AI, and adoption practices for AI-powered talent solutions. - Partner with technology teams to integrate agentic workflows into Talent operating models and business processes. - Drive experimentation and innovation through AI use cases that unlock measurable business value. Data Products & Platform Leadership - Own the vision, roadmap, and lifecycle management of Talent analytics products and self-service insight solutions. - Lead development of enterprise Talent data products designed for broad organizational consumption. - Establish product management disciplines, user-centric design practices, and adoption strategies across Talent Intelligence initiatives. - Drive the transition from static reporting toward intelligent, scalable, reusable analytics products. - Identify opportunities to industrialize analytics capabilities and increase insight accessibility across stakeholder groups. - Enable scalable analytics architecture supporting structured and unstructured talent data. - Collaborate with Data Engineering and Technology teams to optimize data quality, governance, lineage, accessibility, and security. - Drive modernization of the talent analytics ecosystem through cloud-native analytics capabilities. Insights, Storytelling & Executive Communications - Transform analytical outputs into compelling executive narratives and strategic workforce recommendations. - Create executive dashboards and insight products that simplify complex workforce information. - Develop thought leadership that shapes future Talent Intelligence capabilities and workforce decision-making practices. - Present findings confidently to executive talent leaders and senior business stakeholders. - Drive adoption of analytics by making insights easily understandable, relevant, and actionable. Leadership & Capability Development - Lead and develop a high-performing team of data scientists, workforce strategists, analytics consultants, AI practitioners, and product managers. - Establish talent intelligence standards, methodologies, and governance frameworks. - Foster a culture of innovation, experimentation, continuous learning, and responsible AI usage. - Build organizational capabilities in analytics, AI literacy, data storytelling, and product thinking. - Manage delivery across global, offshore, vendor, and managed-service teams where applicable. Analytical/Decision Making Responsibilities - Determine strategic priorities for Talent Intelligence and Advanced Analytics programs. - Prioritize investments and resource allocation across analytics, AI, and data product portfolios. - Define and monitor key workforce intelligence metrics and success measures. - Evaluate emerging workforce trends, technologies, and market developments to influence strategic direction. - Balance innovation investments with operational scalability, governance, and business value realization. - Ensure responsible and ethical use of workforce data and AI solutions. That's a lot of bullet points. We'll put them all under

• ... each bullet ...

. Now Requirements: Could include "Other Requirements: Due to global nature of the role; travel and willingness to work alternative hours will be required. Due to global nature of the role; English language skills - excellent written and verbal communication will be required." Also maybe "Job Requirements: Education: ..." but that is Qualifications. We'll have:

Requirements

• Due to global nature of the role; travel and willingness to work alternative hours will be required.
• Due to global nature of the role; English language skills - excellent written and verbal communication will be required.

Now Qualifications: Education, Certification Requirements, Experience. We'll list: Education: Bachelor’s degree in data science, Analytics, Statistics, Computer Science, Economics, Business Analytics, Organizational Psychology, Industrial Engineering, Human Resources, or related discipline. Master's degree preferred. Certification Requirements: Higher professional or master’s qualification in a related discipline is preferred, not required. Active membership in related professional bodies or industry groups is preferred, not required. Microsoft certifications in MS Fabric, Copilot, Copilot Studio, AI, Machine Learning, Data Science, or Product Management certifications. Professional certifications related to Planning, People Analytics, or Human Capital Management. Experience: 18 to 20+ years of experience in Talent Analytics, Planning, Talent Intelligence, Data Science, AI, Copilot or related fields. Demonstrated experience building enterprise-scale analytics, AI, or data product solutions as a consultant to external clients for talent domain. Experience implementing Generative AI and Copilot-enabled business solutions. Experience with Microsoft Fabric and modern cloud-based analytics environments. Proven track record of delivering executive-level insights and strategic decision support. Experience leading multidisciplinary global teams. Experience managing complex stakeholder environments and large transformation initiatives. Demonstrated success translating advanced analytics into business outcomes and measurable value realization. We'll put each as

maybe combine? But we need to preserve detail; we can put each bullet as a separate line but still preserve. We'll do:

Qualifications

• Education: Bachelor’s degree in data science, Analytics, Statistics, Computer Science, Economics, Business Analytics, Organizational Psychology, Industrial Engineering, Human Resources, or related discipline. Master's degree preferred.
• Certification Requirements: Higher professional or master’s qualification in a related discipline is preferred, not required. Active membership in related professional bodies or industry groups is preferred, not required. Microsoft certifications in MS Fabric, Copilot, Copilot Studio, AI, Machine Learning, Data Science, or Product Management certifications. Professional certifications related to Planning, People Analytics, or Human Capital Management.
• Experience: 18 to 20+ years of experience in Talent Analytics, Planning, Talent Intelligence, Data Science, AI, Copilot or related fields. Demonstrated experience building enterprise-scale analytics, AI, or data product solutions as a consultant to external clients for talent domain. Experience implementing Generative AI and Copilot-enabled business solutions. Experience with Microsoft Fabric and modern cloud-based analytics environments. Proven track record of delivering executive-level insights and strategic decision support. Experience leading multidisciplinary global teams. Experience managing complex stakeholder environments and large transformation initiatives. Demonstrated success translating advanced analytics into business outcomes and measurable value realization.

Now Skills: Knowledge and Skills Requirements sections. We have sub-sections: Strategic & Business Skills, Analytics & Data Science Skills, AI & Emerging Technology Skills, Data & Technology Skills, Leadership Competencies. We'll list each as bullet preserving details. Strategic & Business Skills: - Deep understanding of Talent Intelligence, Planning, Talent Analytics, Workforce Evolution and Talent Strategy. - Strong consulting, stakeholder management, and executive influence capabilities. - Ability to align talent analytics outcomes with broader business objectives. - Strong commercial acumen and understanding of organizational transformation. Analytics & Data Science Skills: - Advanced statistical analysis and predictive modelling, Machine Learning and AI solution design, Experimental design and causal inference methodologies, Skills intelligence and talent marketplace analytics, Expertise in data storytelling and executive reporting. AI & Emerging Technology Skills: - Generative AI solutions design and implementation, Microsoft Copilot ecosystem expertise, Copilot Studio and conversational AI development, Agentic AI architecture and orchestration, Retrieval-Augmented Generation (RAG) patterns, Prompt engineering and AI governance, Responsible AI and model explainability, Knowledge management and semantic search solutions. Data & Technology Skills: - Data Bricks, Microsoft Fabric, Power BI and advanced visualization. SQL and advanced data modelling. Microsoft Copilot, Copilot Studio, Azure AI, Enterprise GenAI platforms and Machine Learning. Python and R, Modern cloud analytics architectures. API and data integration concepts, Data governance and metadata management. Leadership Competencies: - Executive presence, Strategic thinking, Innovation mindset, Change leadership. Product-centric thinking, Collaboration across global teams, Enterprise influence and stakeholder engagement. Ability to cope with ambiguity; to drive change and performance outcomes in a complex and agile environment. We'll put each as

with the content. Now Benefits: What We Offer You (Canada) paragraph. We need to capture all details: "We offer a competitive compensation package where you’ll be rewarded based on your performance and recognized for the value you bring to