Data Scientist, Payments ReconciliationFinanceBangalore, India

Rippling
RemotePosted 27 February 2026

Job Description

Current Openings Data Scientist, Payments Reconciliation Data Scientist, Payments Reconciliation About Rippling Rippling gives businesses one place to run HR, IT, and Finance. It brings together all of the workforce systems that are normally scattered across a company, like payroll, expenses, benefits, and computers. For the first time ever, you can manage and automate every part of the employee lifecycle in a single system. Take onboarding, for example. With Rippling, you can hire a new employee anywhere in the world and set up their payroll, corporate card, computer, benefits, and even third-party apps like Slack and Microsoft 365—all within 90 seconds. Based in San Francisco, CA, Rippling has raised $1.4B+ from the world’s top investors—including Kleiner Perkins, Founders Fund, Sequoia, Greenoaks, and Bedrock—and was named one of America's best startup employers by Forbes. We prioritize candidate safety. Please be aware that all official communication will only be sent from @Rippling.com addresses. About the role Join our growing Payments Data & Analytics team as an experienced Data Scientist. This is an opportunity to be embedded in the engine room of our customer funds analytics —playing a critical role in reconciling customer data, improving data quality, and making our payments reconciliation platform bulletproof and more scalable. You’ll work closely with Engineering, Product, and Finance teams to ensure customers have clean, actionable data at their fingertips. Your role will involve analyzing complex data sets, managing analytical projects, and collaborating with various teams to deliver complete and accurate analytics to ensure compliance with regulators and external parties. You should have strong critical thinking skills, and the ability to frame and break down complex problems. You thrive under ambiguity and can operate cross functionally in a fast paced environment. You are able to operate across the data stack, and support all elements from data engineering to delivering strategic recommendations What you will do Build internal tooling and processes to reconcile financial and transactional data from multiple sources to enable accurate, repeatable customer funds reporting  Collaborate with key stakeholders (Accounting, Compliance, Engineering, etc.) to understand business requirements and develop solutions to automate reporting and reconciliation, including internal tool development and/or implementation of third party tools. Maintain strong internal controls to protect against payment errors or compliance breaches. Support audits, month-end reconciliation, system implementations and special projects. Leverage data analysis and AI-powered tools to identify process gaps, detect anomalies, and drive automation opportunities. Improve the fidelity and performance of our DBT pipelines and help evolve our broader data architecture What you will need A minimum of 4 years of experience in Business Intelligence/Data Analytics within a Finance, Accounting, or Compliance function Excellent verbal communication and presentation ability. You are able to frame problems, and communicate to all levels of an organization Proven track record of working cross functionally and communicating findings to executive leadership Experience partnering with Finance/Accounting organizations in performing detailed and data intensive reconciliations with disparate datasets. Experience reconciling financial or transactional data (ideally in an e-commerce or payments environment) Experience with data warehousing and reporting technologies like DBT, Snowflake, Tableau, etc. Expert in SQL Familiarity with business intelligence best practices and tooling  Familiarity with data transformation best practices and tooling  (e.g. dbt projects, incremental tables, etc.) Experience with data visualization tools and delivering self service reporting Additional Information Rippling is an equal opportunity employer. We are committed to ... (truncated, view full listing at source)