Risk Data Scientist

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 $1.2B 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

As a Risk Data Scientist on the Credit Risk team at Rippling, you will play a vital role in applying data science techniques to analyze and mitigate credit risks across Rippling’s suite of financial products. You will work closely with the Credit Strategy and Engineering teams to support the development of data-driven risk models and analytics tools that improve credit risk management. This is an excellent opportunity for individuals with strong analytical skills and hands-on experience in data science and risk modeling.

What you will do

• Develop and enhance credit risk models: Support the creation, testing, and improvement of credit risk models for financial products such as Corporate Card, Payroll, and Bill Pay. Leverage data from internal and external sources to build new data sets and improve risk management capabilities.
• Conduct risk analysis: Analyze customer behavior, financial data (e.g., bank transactions, payroll), and risk patterns to identify emerging risks and contribute to risk mitigation strategies.
• Data preparation and feature engineering: Assemble, clean, and preprocess data to create features that power credit risk models. You’ll work with large, complex datasets and ensure they are structured for analysis and modeling.
• Collaborate with cross-functional teams: Work alongside Product, Credit, and Engineering teams to ensure that data-driven insights are integrated into product development and operational workflows.
• Evaluate credit risk strategies: Assist in measuring the success of risk strategies using KPIs, and recommend adjustments to improve outcomes.
• Assist with reporting: Prepare and communicate findings from your analyses to key stakeholders, helping to inform decision-making and strategic direction.

What you will need

• 3+ years of experience in data science and analytics: Proven ability to use data science methods to address risk-related challenges, particularly in the financial or fintech industries.
• Educational background: Bachelor’s degree in a relevant field such as Data Science, Mathematics, Statistics, or a related discipline.
• Proficiency in data analysis tools: Hands-on experience with Python, R, SQL, and other data analysis tools. You should be comfortable performing statistical analysis, running queries, and building models.
• Strong analytical and problem-solving skills: You will be expected to turn complex data into actionable insights that influence credit risk management decisions.
• Strong communication skills: The ability to communicate technical findings clearly to both technical and non-technical stakeholders.

Nice to have

• Understanding of credit risk: Some exposure to credit risk or financial analysis, such as underwriting or financial statement analysis, is preferred.
• Experience with machine learning: Familiarity with machine learning models (e.g., logistic regression, decision trees, random forests) to build and enhance predictive models is a plus.