Staff Data Scientist, Finance

GoFundMe • San Francisco, CA

3h • $179,500 - $269,500 • Hybrid

About The Position

GoFundMe is the world’s most powerful community for good, dedicated to helping people help each other. By uniting individuals and nonprofits in one place, GoFundMe makes it easy and safe for people to ask for help and support causes—for themselves and each other. Together, our community has raised more than $40 billion since 2010.
We're looking for a Staff Data Scientist, Finance to own the data science strategy for Accounting, Finance, and Payments at GoFundMe. This role sits at the intersection of financial analytics, payments intelligence, and operational automation—with direct responsibility for building the models, insights, and AI-driven systems that power financial decision-making across the company. You'll define the technical roadmap, drive high-impact initiatives from fraud detection to revenue forecasting, and build the analytical foundation that scales with the business.
Candidates considered for this role will be located in the San Francisco Bay Area. There will be an in-office requirement of 3x a week.

Requirements

• 8+ years of experience in data science, analytics, or a related quantitative role, with demonstrated ability to own end-to-end data initiatives.
• Master's or Ph.D. in a quantitative discipline (Statistics, Economics, Mathematics, Computer Science, Engineering, or related field) or equivalent practical experience.
• Energized by the opportunity to build from the ground up—establishing the analytical foundation while shaping the long-term vision for financial data science.
• Strong foundation in financial modeling, forecasting, and quantitative analysis.
• Experience with anomaly detection, time-series forecasting, or classification applied to financial data.
• Familiarity with accounting and finance concepts (revenue recognition, reconciliation, financial close).
• Demonstrated ability to translate business problems into analytical frameworks and measurable outcomes.
• Advanced proficiency in SQL (complex queries, window functions, performance optimization) and Python (pandas, NumPy, scikit-learn).
• Strong hands-on experience with data pipeline and transformation tools, particularly dbt; familiarity with orchestration tools (Airflow, dbt Cloud) is a plus.
• Familiarity with data warehousing platforms (Snowflake, Databricks, BigQuery) and modern analytics infrastructure.
• Demonstrated ability to leverage modern AI tools and coding agents (e.g., LLM-based assistants, autonomous coding agents, AI-driven data quality tools) to accelerate development and automate workflows.
• Experience designing or applying AI-assisted solutions to financial automation problems (e.g., intelligent document processing, anomaly explanation, process orchestration).
• Strong working knowledge of machine learning techniques for forecasting, anomaly detection, classification, and fraud modeling; proven ability to apply these to financial data problems.
• Exceptional communication skills: able to influence non-technical stakeholders and present complex analyses to executive audiences.
• Demonstrated ability to work independently, manage ambiguity, and drive initiatives in a cross-functional environment.
• Strong partnership orientation—comfortable embedding with business teams (Accounting, Finance, Payments) and translating their needs into data solutions.

Nice To Haves

• Previous experience in FinTech, Payments, or Accounting data environments.
• Experience with financial systems (e.g., NetSuite, SAP, Stripe, Adyen).
• Familiarity with accounting processes (GAAP, revenue recognition, reconciliation).
• Experience with financial forecasting models (budgeting, P&L).
• Payments domain knowledge (transaction flows, chargebacks, fraud patterns). Deep understanding of payments systems, transaction flows, and fraud/risk patterns.
• Expertise in data visualization and BI tools (Looker, Tableau, or equivalent); strong storytelling and presentation abilities.

Responsibilities

• Build AI-powered automation systems: Design and deploy AI/ML solutions—including LLM-based workflows—to automate financial close processes, accelerate reporting cycles, and improve data quality at scale.
• Own the financial data foundation: Architect and maintain the data pipelines, models, and dashboards that enable Accounting, Finance, and Payments teams to operate with accuracy, speed, and confidence.
• Drive financial forecasting and scenario modeling: Build and refine revenue, donation, and cash flow forecasting models that power FP&A, treasury, and executive planning. Develop scenario analyses that inform strategic decisions.
• Lead payments intelligence initiatives: Design fraud detection and prevention models, optimize payment processor routing and costs, and build chargeback prediction systems that protect revenue and reduce losses.
• Drive anomaly detection and risk monitoring: Develop models to surface irregularities in financial transactions, identify cost or revenue leakage, and support audit and compliance requirements.
• Uncover strategic finance insights: Conduct deep-dive analyses on unit economics, geographic and segment-level performance, and fee structure optimization to inform business strategy.
• Shape the technical roadmap: Define best practices for financial data modeling, pipeline architecture, and analytics infrastructure; elevate data literacy and self-service capabilities across finance functions.
• Influence cross-functional strategy: Serve as the trusted data science advisor to Accounting, Finance, and Payments leadership, translating complex analyses into actionable recommendations for executive audiences.

Benefits

• Enjoy competitive pay and comprehensive healthcare benefits.
• Enjoy financial assistance for things like hybrid work, family planning, along with generous parental leave, flexible time-off policies, and mental health and wellness resources to support your overall well-being.
• Participate in learning, development, and recognition programs to help you thrive and grow.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level