Staff Data Scientist, Finance & Business Ops

What you'll do

• Own forecasting tooling end to end: Build and maintain the team's primary forecasting workbench from underlying data and forecast logic through the interactive web UI that planners use to create, adjust, and review forecasts. This includes baseline vs. adjusted forecast modeling, scenario/delta workflows, backtesting, and diagnostics (year-over-year and month-over-month seasonality, engagement rates).
• Ship product, not just analysis: Design and build user-facing features such as charts and visualizations, guided onboarding, history/audit views, region and time-grain filtering, performance optimization, and polish/bug-fixing. Instrument usage with raw logs and let adoption data drive the roadmap.
• Drive AI adoption across Finance & BizOps: Turn platform-level AI capabilities into concrete, trusted tools for finance users. Bring structured business cases to platform/IT partners, pilot new capabilities, and create enablement materials including walkthroughs, documentation, and "where to get started" guidance.
• Stay ahead of the AI capability curve: Continuously read and interpret AI research (papers, model and tooling releases) and translate into a grounded point of view on what will be possible in the next 6–12 months. Track the engineering roadmap and connect platform capabilities to concrete opportunities for the CFO org.
• Set AI strategy and guide executives: Shape the CFO org's AI roadmap, prioritize investments, advise senior leaders on what's real vs. hype, and communicate complex AI and technical trade-offs in plain, decision-ready terms.
• Deliver recurring finance analytics: Support budget-vs-actuals (BVAs), variance commentary, executive slide/deck preparation, metric diagnostics (e.g., MAU and revenue), and resolve 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 release notes and 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 durable artifacts and documentation.

What we’re looking for

Data science & forecasting

• Strong applied background in time-series forecasting and quantitative analysis: baseline construction, scenario/adjustment modeling, backtesting, 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) with 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

• 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.