Rippling
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Fraud Risk Data Scientist
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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. As a Fraud 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. What You Will Do 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. Analyze fraud patterns and risk trends: Perform deep analysis on transactional data to identify fraud patterns, emerging threats, and vulnerabilities. Collaborate across teams: Work closely with Fraud Risk Strategy, Security, Product, and Engineering teams to align fraud prevention initiatives with business goals. Conduct comprehensive cost-benefit analyses: Evaluate the trade-offs between fraud risk reduction, customer experience, and operational efficiency. Enhance ATO detection through data analysis: Use data analytics to identify suspicious behaviors and enhance ATO detection. Monitor and refine risk strategies: Continuously assess and improve fraud detection strategies based on new data insights, fraud trends, and ongoing performance evaluations. What You Will Need 1+ year of experience in data science and analytics: Demonstrated experience in using analytics and data science techniques to solve fraud-related challenges. 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. 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. Effective cross-functional collaboration: Proven ability to collaborate with product, risk, security, and engineering teams to drive fraud risk initiatives. Educational background: Bachelor's degree in a relevant field such as Data Science, Mathematics, Statistics, or Operations Research. Nice to Have Experience with machine learning models: Familiarity with building and deploying machine learning models for fraud detection. Experience in SaaS or FinTech environments: Prior experience working in a fast-paced, tech-driven environment with a focus on financial services or SaaS. 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.
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Fraud Risk Data Scientist
role at
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. As a Fraud 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. What You Will Do 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. Analyze fraud patterns and risk trends: Perform deep analysis on transactional data to identify fraud patterns, emerging threats, and vulnerabilities. Collaborate across teams: Work closely with Fraud Risk Strategy, Security, Product, and Engineering teams to align fraud prevention initiatives with business goals. Conduct comprehensive cost-benefit analyses: Evaluate the trade-offs between fraud risk reduction, customer experience, and operational efficiency. Enhance ATO detection through data analysis: Use data analytics to identify suspicious behaviors and enhance ATO detection. Monitor and refine risk strategies: Continuously assess and improve fraud detection strategies based on new data insights, fraud trends, and ongoing performance evaluations. What You Will Need 1+ year of experience in data science and analytics: Demonstrated experience in using analytics and data science techniques to solve fraud-related challenges. 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. 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. Effective cross-functional collaboration: Proven ability to collaborate with product, risk, security, and engineering teams to drive fraud risk initiatives. Educational background: Bachelor's degree in a relevant field such as Data Science, Mathematics, Statistics, or Operations Research. Nice to Have Experience with machine learning models: Familiarity with building and deploying machine learning models for fraud detection. Experience in SaaS or FinTech environments: Prior experience working in a fast-paced, tech-driven environment with a focus on financial services or SaaS. 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.
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