Staff Technical Program Manager, Monetization Data Science

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🕒 April 23

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Zigsaw

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2016

Making Job-search and talent discovery simpler, faster and effective for Job-seekers.

📋 Description

• 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.

🏖️ Benefits

• Equity • Flexible work arrangements • Professional development opportunities