Staff Technical Program Manager, Monetization Data Science

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 were 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 others unique experiences and embrace theflexibility to do your best work. Creating a career you love Its Possible.

At Pinterest AI isnt just a feature its a powerful partner that augments our creativity and amplifies our impact and were looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities well 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 itconnecting pinners with ideas and products they love. Within EPD the Monetization org builds the ads and merchant ecosystem that funds Pinterests 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). Whats exciting is the teams 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 youll 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 executionproducing 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.

What were looking for:

• Staff-level TPM scope and behaviors: proven ability to independently own multi-team multi-quarter technical programs including resolving ambiguity driving decisions and delivering outcomes through influence.
• Deep cross-functional leadership: strong partnership with Product and Engineering plus ability to align Design Sales PMM Core Platforms and Data on sequencing tradeoffs and adoption.
• Data platform metrics judgment: experience building trusted metrics/SSOT and operational cadences that shift org behavior toward leading indicators and fast diagnosis.
• Mechanism builder not process administrator: track record of creating durable operating systems (cadence dashboards decision logs RACI/DRIs) that reduce toil and increase velocity.
• Excellent risk and dependency management: anticipates cross-org failure modes keeps stakeholders aligned with crisp comms and escalates with clear options and recommendations.
• AI-first execution mindset: demonstrated ability to use GenAI to accelerate planning program operations and stakeholder communicationsstarting with AI drafts and applying strong judgment to validate refine and drive decisions.
• Workflow design AI fluency data & insights orientation: experience turning repeatable program work into durable low-toil mechanisms and improving decision-making by using GenAI (e.g. strong prompting vibe coding lightweight scripts/tools dashboards data analysis and leveraging agents where appropriate)
• Safety-by-design AI fluency: experience operating within AI governance expectations (risk assessment data handling model/output validation auditability/traceability) and proactively identifying where AI use is not appropriate or requires additional controls.
• Bachelors degree in Computer Science Engineering a related field or equivalent experience.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit ourPinFlex page to learn more about our working model.

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1-2 times every 6-months and therefore can be situated anywhere in the country.

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