Personalization Product Manager - Machine Learning Platform - Vice President

You care about how fast good models reach customers, and you know most of that time is lost after the science is done.

Join us to make getting a model to production fast, repeatable, and safe.

As a Vice President within the Personalization & Customer Insights (Intelligence) team, you help shape the vision for our modeling platform and own the roadmap for key parts of it.

You are the voice of the modeler, the data scientists and ML engineers who build on the platform.

You work with platform leadership and engineering to set direction, and you drive execution across Data Science, ML Engineering, Cloud and Infrastructure, Model Governance, and the product teams that depend on the models you serve.

You are accountable for what your areas ship, on time and at quality.

The platform serves models three ways: batch serving on a schedule, real-time serving in the moment a customer acts, and language model hosting for the small and large language models behind AI-written messages and assistants.

Each comes with the reporting that makes it a product, not just infrastructure: performance, monitoring, explainability, cost and usage, service health, and the evidence model governance needs.

The goal is speed to market.

Today, getting a model into production is too often a custom project.

This role helps make it the default path, so a trained model is served, monitored, and ready to test in days, not months.

Job responsibilities


* Help shape the platform strategy and own the roadmap for your areas of the model development lifecycle (MDLC), from deployment and serving to monitoring and governance.


* Cut time to market: shorten the time from trained model to production and grow the share of models that go live without custom infrastructure work.


* Own the roadmap for the serving paths in your scope (batch, real-time, and language model hosting), including the response time, uptime, and cost each one commits to.


* Help make going live self-serve: one path to train, deploy, serve, and A/B test, with no ticket to another team, including for teams outside personalization.


* Own the reporting that ships with your areas: performance, monitoring and drift, explainability, cost and usage, and service health, built for model owners, product stakeholders, and model governance.


* For hosted language models, define what teams get out of the box: routing between self-hosted and external models, evaluation, tracing, guardrails, and cost reporting.


* Prioritize against real constraints, balancing new capabilities with reliability, controls, regulatory needs, and technical debt.


* Be accountable for delivery: your roadmap shipping to production, on time and at quality.


* Partner with engineering on architecture and build-versus-buy decisions, staying close enough to the detail to pressure-test them.


* Partner with Risk, Model Governance, and Compliance to build governance into the platform, automated where possible and trus...