Director, Growth AI Platforms & Strategy

EY-Parthenon

New York (NY)

Hybrid

USD 205,000 - 235,000

Full time

14 days+

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Benefits offered by this job

Hybrid work model
Medical and dental coverage
401(k) and Pension

Job summary

EY-Parthenon, part of EY, seeks a product leader to build and scale neuro‑symbolic AI products for enterprise clients. You will guide cross-functional teams across product, engineering, data science, and industry specialists, defining strategy, roadmaps, and success metrics.

You will ensure explainability, auditability, and regulatory compliance while shaping pricing, deployment models in a hybrid work environment.

Qualifications

  • Bachelor’s degree in a relevant field and 5 years of related work experience; or a graduate degree with ~3 years of related work experience.
  • 7+ years in product leadership, solution delivery, enterprise technology, or platform-building roles.
  • Proven experience building and launching enterprise-grade products.
  • Willingness to travel and work beyond standard hours.
  • Residence in or commutable to the office location for this position.

Responsibilities

  • Lead the build and scaling of neuro‑symbolic AI products across the lifecycle (problem framing, requirements, architecture, build, deployment, scale).
  • Define product strategy, roadmap, and commercial outcomes; shape pricing, packaging, and commercialization pathways.

Skills

Knowledge graphs
Symbolic reasoning
ML integration patterns
Data governance

Education

Bachelor’s degree in Business/Statistics/Economics/Mathematics/Engineering/CS/Analytics
Graduate degree (optional)

Tools

Azure
AWS
GCP

Job description

EY-Parthenon, part of EY, seeks a product leader to build and scale neuro‑symbolic AI products for enterprise clients. You will guide cross-functional teams across product, engineering, data science, and industry specialists, defining strategy, roadmaps, and success metrics.

You will ensure explainability, auditability, and regulatory compliance while shaping pricing, deployment models in a hybrid work environment.

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