Staff Technical Program Manager, Monetization Data Science
Quick summary
- Work type
- Remote
- Location
- Remote
- Salary
- $145,747–$300,067 / yr
- Posted
- 7 days ago
- Freshness
- Confirmed live 2 days ago
Market check
Salary context
How this pay compares to similar roles
This role pays more than 74% of similar roles. Most pay $177,250–$224,075 — the shaded band above. At the midpoint, this role pays about $223k versus about $201k for comparable roles.
Based on 240 similar postings.
Employer
About Pinterest
Pinterest is a visual discovery and inspiration platform where people find ideas for home, style, recipes, and more. It serves hundreds of millions of users worldwide through its image and video pinboard product.
Pinterest currently has 82 open roles on FindRole.
Listed pay typically runs $164,695–$337,990 across 82 roles with salary data.
Most-posted roles
- Software Engineer 18
- Machine Learning Engineer 11
- Data Scientist 8
- Technical Program Manager 6
- Product Manager 5
At a glance
TL;DR · Staff Technical Program Manager, Monetization Data Science
Staff Technical Program Manager, Monetization Data Science serves as the lead for the Monetization Data Science team within a cross-functional network of Product, Engineering, Design, Sales, and Platforms. This role focuses on building a data-driven monetization engine by unifying fragmented data into a single source of truth, establishing an end-to-end input metrics funnel, and democratizing analytics to improve decision-making. Key responsibilities include productionalizing data science strategies, automating workflows, improving delivery funnel instrumentation, and executing deep-dive programs on topics like auction density. The role requires expertise in technical program management, risk and dependency management, and building durable operating systems. Candidates must demonstrate an AI-first execution mindset, utilizing GenAI for drafting artifacts and "vibe coding" to build tools while adhering to safety-by-design principles. Required skills include data platform knowledge, metric judgment, and experience with complex cross-functional leadership.
What does a Technical Program Manager earn in Remote?
Median $210285 from 30 postings across 17 companies.
What you'll do
- Lead the Monetization Data Science execution roadmap across four strategic pillars including data sources, segmentation, and analytics.
- Coordinate with Engineering and Data teams to productionalize core tables, governance, and scalable data pipelines.
- Partner with Engineering to close observability gaps and improve delivery funnel instrumentation for reliable analysis.
- Automate recurring data workflows and program operations to reduce manual intervention and improve monitoring.
- Develop partner-facing tools and dashboards to democratize analytics and facilitate faster decision-making.
- Establish business review cadences that hold teams accountable for moving controllable input metrics.
- Execute cross-functional deep-dive programs to identify specific opportunities and develop actionable downstream plans.
- Integrate GenAI into the operating model to automate program artifacts, triage tasks, and synthesize risk signals.
What we're looking for
- Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience.
- Proven ability to independently own multi-team, multi-quarter technical programs at a Staff level.
- Experience building trusted metrics and single source of truth (SSOT) systems for data platforms.
- Track record of creating durable operating systems and mechanisms to reduce manual toil and increase velocity.
- Demonstrated ability to use GenAI tools to accelerate planning, program operations, and stakeholder communications.
- Proficiency in AI fluency including prompting, "vibe coding" scripts/tools, and ensuring safety-by-design governance.
- Experience managing complex risks and dependencies while communicating clearly with cross-functional partners like Product, Engineering, and Sales.
- Ability to translate repeatable program work into automated workflows and durable mechanisms.
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