Risk Data Analytics and Operations
Rippling is the first way for businesses to manage their HR & IT — from payroll and benefits, to employee computers and apps — all in one, modern system.
In just 90-seconds, a company can set up (or disable) an employee’s payroll, health insurance, work computer, and third-party apps, like Gmail, Microsoft Office, and Slack. It’s the only platform that truly unifies every employee system, and automates all of the administrative work.
Rippling is headquartered in San Francisco and has raised over $200M from top-tier investors, including Founders Fund, Greenoaks Capital, Coatue Management, Kleiner Perkins, and YCombinator.
About The Role
This is a unique opportunity to drive the analytical frameworks that will support the future growth of Rippling, a high-growth SaaS company in the HR and IT space. Rippling supports thousands of customers and an even larger number of employees across the SMB to mid-market segment. Our Risk team plays a crucial role in ensuring the viability of our products, financial success of the company, and maintaining a safe and healthy financial ecosystem. As the first analytics hire for Risk, you'll be a founding member of the team dedicated to managing and controlling the fraud, credit, and operational risks we face. As our business grows we’ll be looking for this role to use data to enhance our risk-related decision making and streamline our processes.
What You'll Do:
Act as the primary data resource for Rippling's Risk team, defining and monitoring metrics, creating data narratives, and building tools to drive risk decision making
Work with the product and engineering teams to become the subject matter expert on risk-related data elements
Partner with Data Engineering to design, build, and maintain risk data models
Work with Risk colleagues to design, build, and maintain scalable automation solutions for dashboards and reporting
Perform root-cause analyses on out of-pattern trends and risk-related customer issues; conduct ad-hoc analyses as needed
Develop and manage a regular cadence of metrics reporting and associated distribution methods
Drive the collection of new data and the refinement of existing data sources
Qualifications:
3 to 5 years of experience as a data scientist or data analyst within the payments or fintech space, primarily in support of risk or trust and safety efforts
Advanced SQL skills and a strong understanding of a scientific programming language (R or Python)
Prior experience working with visualization and/or BI tools in support of a business unit
Experience structuring data models and working with data engineers to build scalable solutions
Basic knowledge of basic risk concepts in fraud and/or credit
Self-starter with excellent time management skills
Organized, attentive to detail, and intellectually curious
Even if you don’t meet all of the requirements listed here, we still encourage you to apply. Skills can be used in lots of different ways and your life and professional experience may be relevant beyond what a list of requirements will capture.
Rippling is an equal opportunity employer.