EY EY-Parthenon - Strategy Execution Growth 

United States, New York, New York 

650412582

With deep functional and sector expertise, paired with innovative AI-powered technology and an investor mindset, we partner with CEOs, Boards, Private Equity and Governments every step of the way – enabling you to shape your future with confidence.

Your key responsibilities

· Drive end-to-end development of agentic products that combine internal, public, and licensed third-party data.

· Translate ambiguous business goals into clearly defined agent roles, workflows, data requirements, and interface specs.

· Own product roadmap and use case prioritization based on technical feasibility, customer impact, and data availability.

· Collaborate with AI/ML Engineers to validate model fit, feature strategy, and insight generation logic.

· Work with Data Engineers to assess data availability, coverage, and merge logic across insurance, consumer, market, sector and macroeconomic signals.

· Partner with DevOps/Platform teams to ensure secure, scalable, compliant delivery of outputs (dashboards, APIs, notebooks, or alerts).

· Define POC and MVP requirements and success metrics for each agent product or data application.

To qualify for the role, you must have

· A bachelor's degree in Business, Statistics, Economics, Mathematics, Engineering, Computer Science, Analytics, or other related field and 5 years of related work experience; or a graduate degree and approximately 3 years of related work experience

· Product management experience, ideally in data-intensive or AI/ML-driven platforms.

· Strong experience and understanding of data product lifecycle — ingestion, cleaning, merging, modeling, serving.

· Proven experience leading products that sit at the intersection of business insight, data science, and engineering.

· Strong understanding of real-world structured data merging, schema linking, and model evaluation at scale.

· Familiarity with the concepts behind agentic AI workflows, RAG, or data fusion for insight delivery.

· Ability to communicate clearly across technical and non-technical stakeholders — from model latency to customer value.

· Deep comfort working with multi-sector data — e.g., insurance filings, economic indicators, behavioral signals.

· The ability and willingness to travel and work in excess of standard hours when necessary.

Ideally, you’ll also have

· Experience deploying AI/ML products or features into a production.

· Experience working in a startup and/or management/strategy consulting.

· Knowledge of how to leverage AI tools in a business setting, including Microsoft Copilot.

· Collaborative, problem-solving, and growth-oriented mindset.

What we look for

Deep data exposure, product managers who help define the "how", and expertise with understanding underlying ML workflows.