Staff Technical Program Manager, Monetization Data Science

Zigsaw

Staff Technical Program Manager driving Monetization Data Science execution roadmap at Pinterest. Collaborating across functions to enhance the data-driven monetization engine.

Posted 4/23/2026full-timeSan Francisco • California • 🇺🇸 United StatesLead💰 $145,747 - $300,067 per yearWebsite

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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 communications—starting 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.
Bachelor’s degree in Computer Science, Engineering, a related field or equivalent experience.

Benefits

Comp & perks

Equity
Flexible work arrangements
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data analysisworkflow automationdashboard creationmetrics judgmentAI-assisted workflowsrisk assessmentdata governanceprogram managementobservability instrumentationprocess design

Soft Skills

cross-functional leadershipinfluencecommunicationproblem-solvingstakeholder alignmentdecision-makingmechanism buildingrisk managementcollaborationadaptability