> Markdown version of [/jobs/ext/2709012-staff-technical-program-manager-monetization-data-science](https://www.wearedevelopers.com/jobs/ext/2709012-staff-technical-program-manager-monetization-data-science). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Technical Program Manager, Monetization Data Science - **Company:** Pinterest - **Location:** San Francisco, CA, United States (Remote available) - **Salary:** $145,747.0 - $300,067.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Data Infrastructure, Workflow Management Systems, Data Processing, Information Technology, Data Analytics - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/staff-technical-program-manager-monetization-data-science-pinterest-7943614 ## About the Role * 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. ## Description 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., * 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. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Implementing continuous delivery in a data processing pipeline](https://www.wearedevelopers.com/videos/73-implementing-continuous-delivery-in-a-data-processing-pipeline) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) - [How Data is Shaping our Games](https://www.wearedevelopers.com/videos/176-how-data-is-shaping-our-games) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 129 - Now that's what I call private data!](https://www.wearedevelopers.com/magazine/468-dev-digest-129-now-that-s-what-i-call-private-data) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)