VP, Personalization ML Platform — Faster Model Delivery

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 180,000 - 250,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Vice President to lead product management for a Model Platform within the Personalization & Customer Insights (Intelligence) team in Jersey City.

You will shape the platform vision, own roadmaps, and collaborate with ML engineers, data scientists, and risk/compliance partners to deliver scalable inference and governance solutions. You will drive speed to market, ensure testable production readiness, and define KPIs for adoption, reliability, and cost effectiveness

Qualifications

• 5+ years of product management experience or equivalent leading technical/platform products.
• Proven success delivering ML platform or MLOps products to production.
• Strong understanding of production model lifecycle, latency, throughput, and cost tradeoffs.
• Experience with language models in production: hosting/serving and cost management.

Responsibilities

• Shape the platform strategy and own the roadmap for model development lifecycle.
• Reduce time to production and increase live deployment of models.
• Own serving paths: batch, real-time, and language model hosting with cost and uptime commitments.
• Enable self-serve deployment, training, serving and A/B testing across teams.

Skills

Product management

ML platform

MLOps

Stakeholder management

SQL/Python

Communication

Leadership

Ambiguity tolerance

Education

BS in Computer Science

MS in Data Science or related field

Tools

Databricks

MLflow

Ray

Kubernetes

AWS

Job description

JPMorgan Chase & Co. seeks a Vice President to lead product management for a Model Platform within the Personalization & Customer Insights (Intelligence) team in Jersey City.

You will shape the platform vision, own roadmaps, and collaborate with ML engineers, data scientists, and risk/compliance partners to deliver scalable inference and governance solutions. You will drive speed to market, ensure testable production readiness, and define KPIs for adoption, reliability, and cost effectiveness