Staff Technical Program Manager, Monetization Data Science

Confirmed live in the last 24 hours

San Francisco, CA, US; Remote, US

Remote

Posted April 23, 2026

Job Description

About Pinterest:

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

The Team:

Pinterest helps people find inspiration and take action on it—connecting pinners with ideas and products they love. Within EPD, the Monetization org builds the ads and merchant ecosystem that funds Pinterest’s business while protecting long-term user experience. This Staff TPM role sits in Monetization as the TPM lead for Monetization Data Science, at the center of a highly cross-functional network (Product, Engineering, Design, Sales, PMM, Core, Platforms, Data). What’s exciting is the team’s explicit shift toward a “data-driven monetization engine”: unifying fragmented data into a trusted SSOT, building an end-to-end input metrics funnel, enabling advanced segmentation, and democratizing analytics so teams can move faster and make better decisions with shared context.

What you’ll do:

Lead the Monetization DS execution roadmap: drive the integrated plan across the four strategic pillars (SSOT + funnel, segmentation, input-metrics cadence, democratized analytics) with clear milestones and success measures.
Productionalize our DS strategy: coordinate Platforms/Data Eng + Monetization Eng + DS to productionalize core tables, governance, reliability, and scale beyond DS-owned pipelines.
Enable new instrumentation: partner with Engineering to close observability gaps (especially delivery funnel instrumentation) so full-funnel survivability can be analyzed reliably.
Drive workflow automation: reduce manual human intervention in recurring data workflows and program operations; build durable mechanisms for monitoring, alerting, and dependency tracking.
Scale self-serve and democratization: deliver partner-facing tooling (dashboards / analytics surfaces) that makes staples the common language and supports fast diagnostics and opportunity mining.
Operationalize input metrics: establish/upgrade business review cadences so teams set goals and are accountable for moving controllable input metrics (not just reporting revenue outcomes).
Drive targeted deep dives: structure and execute cross-functional deep-dive programs (e.g., influencer population, auction density/demand) with clear hypotheses, decision asks, and downstream action plans.
Use GenAI as the default operating model for EP PgM execution—producing AI-assisted first drafts of core program artifacts, modernizing high-toil workflows into AI-first mechanisms (e.g., intake triage, status synthesis, action/decision extraction, risk & dependency tracking), and synthesizing signals to proactively surface risks, decision/trade-offs, and escalation paths.
Prototype solutions to augment decisions through data (e.g. dashboards, data analysis) or simplify processes (e.g. process and workflow helpers, or internal tools) using AI coding assistants (“vibe coding”).
Follow Pinterest AI guidance for risk, governance, and safety-by-design: appropriately handle sensitive data, validate AI-generated outputs, document assumptions/limits, and ensure AI-assisted workflows meet applicable policy/compliance expectations before broad adoption