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About This Role
Location: New York Other locations: Anywhere Globally Salary: Competitive Date: Sep 17, 2026 Job description
Requisition ID: 1742205 At EY, we’re all in to shape your future with confidence.
We’ll 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.
The opportunity
The Talent Advanced Analytics Lead is responsible for shaping and delivering the organization's next-generation Talent Intelligence and Advanced Analytics strategy. This role combines executive leadership advisory, advanced analytics, AI innovation, talent intelligence, and data product delivery to enable leaders to make faster, more informed decisions to unlock value and drive business impact.
The role serves as a strategic advisor to global Talent, Function, Service Line, and Sector leaders by transforming talent, business, operational, and external market data into actionable intelligence. The individual will lead the development of scalable analytics products, AI-powered insights, and agentic solutions that enhance planning, skills intelligence, talent mobility, employee experience, productivity, and organizational effectiveness.
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.
Knowledge and Skills Requirements:
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.
Supervision Responsibilities:
- Work closely with the Leads across Global Talent and wider Talent Functions to ensure the provision of services that support business and functional delivery.
- Lead / manage a team of expert data analytics specialists responsible for functional execution, also build skills and capabilities of the team.
- Develop AI capabilities, define key AI competencies for the team, and ensure provision of learning to advance skills
- Independently maintain and leverage (when appropriate) an internal network, including effective partnerships with senior stakeholders, across EY practices / functions that will enable personal effectiveness in the position.
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.
Job Requirements:
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.
This job posting relates to an existing vacancy within our organization.
What we offer you (Canada)
We offer a competitive compensation package where you’ll be rewarded based on your performance and recognized for the value you bring to our business. In addition, our Total Rewards package allows you to decide which benefits are right for you and which ones help you create a solid foundation for your future. Our Total Rewards package includes a discretionary bonus program, a comprehensive medical, prescription drug and dental coverage plan, a defined contribution pension plan, a great vacation policy plus firm paid days that allow you to enjoy longer long weekends throughout the year, statutory holidays and paid personal days (based on province of residence), and a range of exciting programs and benefits designed to support your physical, financial and social well-being. Plus, we offer:
- Support and coaching from some of the most engaging colleagues in the industry
- Learning opportunities to develop new skills and progress your career
- The freedom and flexibility to handle your role in a way that’s right for you
Are you ready to shape your future with confidence? Apply today.
To help create the best experience during the recruitment process, please describe any accommodation you may need.
Inclusiveness at EY
Inclusiveness is at the heart of who we are and how we work. We’re committed to fostering an environment where differences are valued, policies and practices are equitable, and our people feel a sense of belonging. We embrace diversity and are committed to combating systemic racism, advancing gender equity and women in leadership, advocating for the 2SLGBTQIA+ community, promoting our neuroinclusion and accessibility initiatives, and are dedicated to amplifying the voices of Indigenous peoples (First Nations, Inuit, and Métis) nationally as we strive towards reconciliation. Our diverse experiences, abilities, backgrounds, and perspectives make our people unique and help guide us. Because when people feel free to be their authentic selves at work, they bring their best and are empowered to build a better working world.
Learn about our commitment to Inclusiveness at https://www.ey.com/en_ca/about-us/corporate-responsibility/equity
What we offer you
The compensation ranges below are provided in order to comply with United States pay transparency laws. Other geographies will follow their local salary guidelines, which may not be a direct conversion of published US salary ranges. At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers.
- We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job is:
- + New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $184,300 to $331,700 + Bay Area California offices – $192,000 to $345,500 + All other offices locations in the US, including Sacramento – $153,600 to $304,100
- Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
- Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances. You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.
Are you ready to shape your future with confidence? Apply today.
- To make the most of your application experience, please limit yourself to two applications within a six-month period.
- EY accepts applications for this position on an on-going basis.
- For those living in California, please click here for additional information.
- At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment.
EY | Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
All in to shape the future with confidence.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1-800-EY-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at [email protected].
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At EY, we use artificial intelligence (AI) tools as one element of our recruitment process to enhance efficiency and improve the candidate experience. While AI supports us in our process, human judgment and decision-making remain integral in our candidate experience. We are committed to the responsible use of AI, and our practices are continuously reviewed and refined to ensure they align with the highest ethical principles and regulatory requirements
To all recruitment agencies: EY does not accept unsolicited resumes from recruitment agencies. Any resumes submitted without a prior agreement or request from our hiring team will not be considered. EY is not responsible for any fees related to unsolicited resumes.
Salary Context
This $153K-$345K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At EY, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($249K) sits 18% above the category median. Disclosed range: $153K to $345K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
EY AI Hiring
EY has 65 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Engineering Manager, AI Architect. Positions span Secaucus, NJ, Sydney, New South Wales, Australia, McLean, VA, US. Compensation range: $142K - $357K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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