Staff Technical Program Manager, Monetization Data Science

Responsibilities

• 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

• 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.