Staff Risk Data Scientist at Datamasked

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 Staff Risk Data Scientist in the Fraud Risk team at Rippling, you will play a key role in using advanced analytics and data-driven insights to identify, assess, and mitigate fraud risks across our financial products. The primary focus of this role is to develop robust data driven risk strategies that enhance fraud detection and prevention capabilities across various product lines, such as Payroll, Bill Pay, and Card Transactions. Risk machine learning model experience is a plus.

What you will do

1. Develop data-driven fraud detection strategies: Use advanced analytics to design and enhance fraud detection strategies that address risk in real-time across multiple financial products, including transaction monitoring, onboarding, and account takeover (ATO) detection.
2. Analyze fraud patterns and risk trends: Perform deep analysis on transactional data to identify fraud patterns, emerging threats, and vulnerabilities, and translate these findings into actionable risk mitigation strategies.
3. Collaborate across teams: Work closely with Fraud Risk Strategy, Security, Product, and Engineering teams to align fraud prevention initiatives with business goals, ensuring analytics-driven decisions are integrated into product development and operational workflows.
4. Conduct comprehensive cost-benefit analyses: Evaluate the trade-offs between fraud risk reduction, customer experience, and operational efficiency to develop optimal fraud prevention strategies.
5. Design cross-product fraud monitoring flows: Develop analytical frameworks that cover Bill Pay, Card, Payroll, and other transaction types, improving fraud risk detection across the product suite.
6. Enhance ATO detection through data analysis: Use data analytics to identify suspicious behaviors and enhance ATO detection, working closely with the security team to define rules, analyze data, and measure effectiveness.
7. Evaluate third-party vendors: Use your analytics expertise to assess and recommend third-party risk vendors that could enhance Rippling’s transaction monitoring and fraud detection capabilities.
8. Monitor and refine risk strategies: Continuously assess and improve fraud detection strategies based on new data insights, fraud trends, and ongoing performance evaluations to stay ahead of emerging fraud tactics.

What you will need

1. 8+ years of experience in data science and analytics: Demonstrated experience in using analytics and data science techniques to solve fraud-related challenges, particularly in financial technology, payments, or SaaS industries.
2. Expertise in data analysis: Proficient in extracting insights from large datasets, with hands-on experience using tools such as Python, R, SQL, and other data analysis platforms to create robust fraud detection strategies.
3. Strong knowledge of fraud risks: Deep understanding of fraud detection methodologies, including those related to ACH, bank transfers, account takeovers, and card transactions.
4. Data-driven approach to decision-making: Experience in developing data-driven strategies that address fraud risks while balancing the impact on customer experience and operational efficiency.
5. Effective cross-functional collaboration: Proven ability to collaborate with product, risk, security, and engineering teams to drive fraud risk initiatives.
6. Educational background: Bachelor's degree in a relevant field such as Data Science, Mathematics, Statistics, or Operations Research. A Master’s degree is preferred.

Nice to have

1. Experience with machine learning models: Familiarity with building and deploying machine learning models for fraud detection.
2. Experience in SaaS or FinTech environments: Prior experience working in a fast-paced, tech-driven environment with a focus on financial services or SaaS is beneficial.

Additional Information

Rippling is an equal opportunity employer. We are committed to building a diverse and inclusive workforce and do not discriminate based on race, religion, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, age, sexual orientation, veteran or military status, or any other legally protected characteristics. Rippling is committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process. To request a reasonable accommodation, please email accomodations@rippling.com.

Rippling highly values having employees working in-office to foster a collaborative work environment and company culture. For office-based employees (employees who live within a 40-mile radius of a Rippling office), Rippling considers working in the office, at least three days a week under current policy, to be an essential function of the employee's role.

This role will receive a competitive salary + benefits + equity. A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.

The pay range for this role is:

156,000 - 273,000 USD per year (US Tier 1)