Pinterest is seeking an experienced Staff Data Scientist to join our Finance & Business Operations team. This is a hybrid data-science / applied-AI / product-engineering role inside Pinterest's CFO organization. It sits at the intersection of forecasting and finance analytics, internal tool-building, and AI adoption — and the person in it is expected to operate across all three.

The core mandate is to make the CFO org's forecasting and planning work faster, be more rigorous, and more self-serve. In practice that has meant owning a forecasting product end to end (data pipeline through user-facing UI), partnering directly with Finance, BizOps, and Core/Monetization stakeholders to embed it in their workflows, and turning the company's emerging AI platform capabilities into tools that finance teams actually use day to day.

This is a high-autonomy, high-trust individual-contributor role with broad cross-functional reach.

What you'll do:

• Own forecasting tooling end to end. Build and maintain the team's primary forecasting workbench — from the underlying data and forecast logic through the interactive web UI that planners use to create, adjust, and review forecasts. This spans baseline vs. adjusted forecast modeling, scenario/delta workflows, backtesting, and diagnostics (year-over-year and month-over-month seasonality, engagement rates, and similar).
• Ship product, not just analysis. Design and build user-facing features: chart and visualization work, guided onboarding, history/audit views, region and time-grain filtering, performance optimization, and the kind of polish and bug-fixing that makes an internal tool feel like a real product. Instrument usage (collect and analyze raw logs) and let adoption data drive the roadmap.
• Drive AI adoption across Finance & BizOps. Take platform-level AI capabilities and turn them into concrete, trusted tools for finance users. Bring structured business cases (not wishlists) to platform/IT partners, pilot new capabilities, and write the enablement material — walkthroughs, documentation, "where to get started" guidance — that gets non-technical teams productive.
• Stay ahead of the AI capability curve. A significant part of this role is forward-looking: continuously read and interpret AI research (papers, model and tooling releases) and translate it into a grounded point of view on what will be possible in the next 6–12 months. Track the engineering roadmap closely, understand what platform capabilities are landing and when, and connect those dots to concrete opportunities for the CFO org — so the team builds for where AI is going, not just where it is today.
• Set AI strategy and guide executives. Turn that capability foresight into strategy: shape the CFO org's AI roadmap, prioritize where to invest, and advise senior leaders and executives on what's real, what's hype, and what to bet on. Communicate complex AI and technical trade-offs in plain, decision-ready terms, and act as a trusted technical advisor in executive conversations.
• Deliver recurring finance analytics. Support core CFO-org deliverables: budget-vs-actuals (BVAs), variance commentary, executive slide/deck preparation, and metric diagnostics (e.g., MAU and revenue diagnostics), including catching and resolving data-quality issues.
• Partner broadly and communicate clearly. Work directly with Finance, BizOps, Monetization, and platform/IT stakeholders. Translate ambiguous business questions into tooling and analysis, post clear release notes and stakeholder updates, and run live walkthroughs and training sessions.
• Set technical and analytical standards. raise the bar on rigor (validation, backtesting, sound metric definitions), make pragmatic build-vs-buy and scope calls, and create artifacts and documentation durable enough to outlive any single contributor.

What we're looking for:

• Data science & forecasting
• Strong applied background in time-series forecasting and quantitative analysis: baseline construction, scenario/adjustment modeling, backtesting and forecast-accuracy evaluation, and seasonality analysis (y/y, m/m).
• Fluency in turning messy business questions into well-defined metrics and diagnostics; rigorous about metric definitions, data quality, and validation.
• Advanced SQL and proficiency in a primary analysis language (Python strongly preferred); comfort working directly with data warehouses and large datasets.
• Engineering & tool-building
• Demonstrated ability to build and ship internal web tools, not just notebooks or one-off analyses — meaningful front-end / full-stack capability (e.g., JavaScript/TypeScript, modern UI frameworks, interactive data visualization).
• Practical product-engineering instincts: UX/usability sense, performance debugging and optimization, handling state/data edge cases, and disciplined release hygiene (testing, build/lint, changelogs).
• Experience building dashboards and self-serve analytics (e.g., Superset, Tableau, Looker, or equivalent).
• Applied AI
• Hands-on experience applying modern AI/LLM tooling to real workflows — prototyping with AI assistants, agentic/MCP-style tooling, or internal AI platforms — and a track record of moving from experiment to adopted tool.
• Ability to build the business case for AI investment and to drive adoption with non-technical users (enablement, documentation, training).
• AI foresight & strategy (critical)
• Demonstrated habit of staying current with AI research and the broader landscape: able to read papers and model/tooling release notes and form a credible, independent view of what will be feasible 6–12 months out.
• Able to interpret an engineering roadmap and reconcile it with where the technology is heading — translating both into a concrete capability plan for the business.
• Strong product/business strategy instincts: prioritizing AI investments, sequencing bets, and distinguishing durable capability from hype.
• Executive influence
• Proven ability to advise and guide senior leaders and executives on technical and AI strategy, and to make complex trade-offs legible to a non-technical executive audience.
• Comfortable being the trusted technical voice in the room — framing decisions, managing expectations, and earning credibility with both finance leadership and engineering/platform partners.
• Scope, ownership & communication
• Staff-level autonomy: can independently scope, prioritize, and deliver multi-month efforts with minimal direction, and make sound trade-off calls.
• Excellent written and verbal communication; can write for executives and for end users, and can run live training and walkthroughs.
• Strong cross-functional collaboration across finance, operations, and technical/platform partners.
• Experience
• Minimum of 8 years of relevant experience in data science, analytics engineering, or applied ML.
• Bachelor's degree in a quantitative field (e.g., statistics, computer science, economics, engineering, math) or equivalent practical experience; advanced degree is a plus.

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 collaboration1-2 times every 6-months and therefore can be situated anywhere in the country.

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