
Data Scientist - Financial Analytics
- San Francisco, CA
- $150,000-180,000 per year
- Permanent
- Full-time
- Collaborate cross-functionally with engineering, accounting, financial partnerships, and product teams to analyze and account for billions of dollars flowing through the Rippling payment platform.
- Build full-cycle analyses using SQL, Python, or other scripting and statistical tools, and develop real-time metrics dashboards to manage key financial and operating levers of the business.
- Monitor payment flows between systems, banks, processors, and inter-company accounts, perform daily account reconciliations, and follow up on any discrepancies.
- React swiftly to emerging issues, summarize facts, and provide recommendations for the timely resolution of critical financial matters.
- Collaborate with key stakeholders (Accounting, Compliance, Treasury, etc.) to understand business requirements and develop scalable solutions for reporting and reconciliation automation, including internal tool development and/or the implementation of third-party tools.
- Develop and maintain comprehensive documentation of reconciliation processes and procedures.
- Prepare and deliver data and reporting solutions supporting month-end close, regulatory & compliance reporting, and Internal and External Audit reporting.
- Communicate findings and recommendations to stakeholders through clear and concise presentations and reports.
- Create, maintain, and ensure the completeness and accuracy of reporting databases and dashboards, and collaborate with data engineering to implement, document, validate, and monitor our evolving data infrastructure.
- Master's degree or Bachelor's degree in Computer Science, Engineering, Statistics, MIS or other quantitative fields.
- 5+ years demonstrated experience in applying statistical analysis, modeling, machine learning and/or exploratory analysis to large datasets, ideally in payments processing, quote-to-cash financial reporting.
- Experience with data warehousing, ETL, and reporting tools (e.g. Snowflake, Tableau, dbt, Dagster).
- Extensive experience with SQL, Python, or other scripting languages and their application to all phases of the data science development process (initial analysis and model development through deployment).
- Experience working with engineering, finance, and accounting teams to assess their data needs and build automated reporting pipelines.
- Strong problem-solving and communication skills, with the ability to communicate findings and recommendations clearly to both technical and non-technical audiences.
- Ability to interface with multiple stakeholders and senior leadership (C-suite) across the organization.
- Bonus points if you have any experience with general accounting principles, with the general ledger close process, and regulatory compliance.