Staff Data Scientist, Finance – Business Ops

Zigsaw

Staff Data Scientist in Finance at Pinterest focusing on AI adoption and forecasting tooling. Collaborating with cross-functional teams to deliver finance analytics and product features.

Posted 7/10/2026full-timeSan Francisco • California • 🇺🇸 United StatesLead💰 $164,695 - $339,078 per yearWebsite

Tech Stack

Tools & technologies

JavaScriptPythonSQLTableauTypeScript

About the role

Key responsibilities & impact

Own forecasting tooling end to end.
Ship product, not just analysis.
Drive AI adoption across Finance & BizOps.
Stay ahead of the AI capability curve.
Set AI strategy and guide executives.
Deliver recurring finance analytics.
Partner broadly and communicate clearly.
Set technical and analytical standards.

Requirements

What you’ll need

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.
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.
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).
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).
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.
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.
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.

Benefits

Comp & perks

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Hard Skills & Tools

Quantitative AnalysisScenario ModelingBacktestingForecast-Accuracy EvaluationData Quality ValidationFull-Stack DevelopmentInteractive Data VisualizationAI/LLM Tooling ApplicationProduct EngineeringMetric Definition

Soft Skills

Clear CommunicationCross-Functional CollaborationExecutive AdvisoryTraining and EnablementTechnical Credibility