Staff Data Scientist, Finance & Business Ops

Pinterest

Confirmed live yesterday High trust
Remote

Quick summary

Work type
Remote
Location
San Francisco, CA
Salary
$164,695–$339,078 / yr
Posted
7 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $164k
This role $252k
$82k most similar roles pay here $367k

This role pays more than 90% of similar roles. Most pay $126,800–$201,839 — the shaded band above. At the midpoint, this role pays about $252k versus about $164k 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

View all roles at Pinterest

At a glance

TL;DR · Staff Data Scientist, Finance & Business Ops

Staff Data Scientist, Finance & Business Ops joins the Finance & Business Operations team as a hybrid data science, applied-AI, and product-engineering role. This position focuses on making forecasting and planning more rigorous and self-serve by owning end-to-end forecasting tools, from data pipelines to user-facing web interfaces. The successful candidate will build internal products featuring interactive visualizations, manage complex time-series forecasting models, and drive AI adoption across the organization. Key responsibilities include translating research into actionable AI strategies for executives and delivering recurring finance analytics like budget-vs-actuals. Required skills include advanced SQL, Python, and front-end capabilities such as JavaScript or TypeScript. The role requires expertise in LLM tooling, data quality validation, and building scalable internal tools to solve complex financial planning problems while navigating the intersection of forecasting, automation, and technical strategy for the CFO organization.

What does a Data Scientist earn in California?

Median $208978 from 66 postings across 27 companies.

See salary data

What you'll do

  • Build and maintain end-to-end forecasting tools including data pipelines, logic models, and user-facing web interfaces.
  • Develop and launch product-focused features like interactive visualizations, guided onboarding, and performance optimizations for internal tools.
  • Translate platform AI capabilities into practical, production-ready tools and enablement materials for non-technical finance teams.
  • Monitor AI research and engineering roadmaps to develop long-term technology strategies for the CFO organization.
  • Advise senior executives on AI investment priorities by communicating complex technical trade-offs in plain language.
  • Deliver core financial analytics including budget-vs-actuals, variance commentary, and metric diagnostics.
  • Translate ambiguous business requirements into technical specifications and high-quality documentation for cross-functional partners.

What we're looking for

  • Minimum of 8 years of relevant experience in data science, analytics engineering, or applied ML.
  • Bachelor's degree in a quantitative field such as statistics, computer science, economics, engineering, or math.
  • Strong background in time-series forecasting, including baseline construction, scenario modeling, and seasonality analysis.
  • Advanced SQL skills and proficiency in a primary analysis language, preferably Python.
  • Experience building internal web tools using front-end/full-stack capabilities like JavaScript, TypeScript, or modern UI frameworks.
  • Hands-on experience applying modern AI/LLM tooling to real workflows and moving from experimentation to adopted tools.
  • Ability to interpret AI research and engineering roadmaps to develop long-term technical strategies for executive leadership.
  • Proven ability to communicate complex technical trade-offs to non-technical stakeholders and senior executives.

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