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Pinterest Data Scientist Jobs (NOW HIRING)

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Pinterest Data Scientist information

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$37.5K

$122.7K

$196.5K

How much do pinterest data scientist jobs pay per year?

As of Jul 2, 2026, the average yearly pay for pinterest data scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What kinds of projects and collaborations can a Pinterest Data Scientist expect in their daily work?

As a Pinterest Data Scientist, you can expect to work on projects that range from developing recommendation algorithms to analyzing user engagement and supporting product feature launches. You'll routinely collaborate with engineers, product managers, and designers to translate analytical insights into product improvements and data-driven strategies. The environment is highly cross-functional, with frequent opportunities to contribute to both short-term experiments and long-term product vision. This collaborative structure offers varied experiences and supports the ongoing development of both technical and business-focused skills.

What are the key skills and qualifications needed to thrive in the Pinterest Data Scientist position, and why are they important?

To thrive as a Pinterest Data Scientist, you need a strong foundation in statistics, machine learning, data analysis, and programming, typically backed by a degree in a quantitative field. Proficiency in tools like Python, R, SQL, Hadoop, and experience with data visualization platforms such as Tableau or Looker are highly valued, as is familiarity with A/B testing methodologies. Strong communication, problem-solving, and cross-functional collaboration skills set outstanding candidates apart. These qualifications are essential for driving data-informed decisions, optimizing user experiences, and supporting business growth on the Pinterest platform.

What is a Pinterest Data Scientist job?

A Pinterest Data Scientist analyzes large datasets to drive product decisions, improve user experiences, and optimize platform performance. They use statistical models, machine learning, and experimentation to uncover insights and guide business strategy. Responsibilities may include A/B testing, recommendation algorithms, and trend analysis to enhance engagement and monetization. Collaboration with engineers, designers, and product managers is key to translating data into impactful decisions.

More about Pinterest Data Scientist jobs
Infographic showing various Pinterest Data Scientist job openings in the United States as of June 2026, with employment types broken down into 89% Full Time, and 11% Part Time. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.
Staff Technical Program Manager, Monetization Data Science

Staff Technical Program Manager, Monetization Data Science

Pinterest

San Francisco, CA

$152K - $196K/yr

Other

Posted 8 days ago


Job description

The Team: 

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.

What we're looking for:

  • 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.

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

  • 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.

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