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Riskified

eCommerce fraud prevention and revenue protection

Junior Data Scientist

Applied Machine LearningUpdated on Nov 12
Full-timeTel Aviv-Yafo, IsraelNo salary listed

About the Job

Requirements

  • A Master of Science degree in Computer Science, Electrical Engineering, or a related quantitative field.
  • At least 2 years of industry experience developing and deploying machine learning models in production environments.
  • Deep theoretical understanding of and practical experience with machine learning algorithms on tabular and structured data.
  • Proficiency in Python, SQL, and standard machine learning frameworks.
  • Strong hypothesis-driven problem-solving skills.
  • Excellent communication skills and the ability to explain complex quantitative concepts to non-technical stakeholders.
  • Direct experience in risk management, e-commerce fraud detection, or fintech domains.

Responsibilities

  • Design, train, evaluate, and optimize scalable predictive models, including classification, regression, clustering, and deep learning, for complex business challenges.
  • Clean, transform, and extract high-value features from large-scale structured and unstructured data to continuously improve model performance.
  • Work within the Data Science team and alongside Data Engineers, Developers, and Product Managers to deploy models into production and translate complex insights into clear business actions.
  • Continuously explore emerging machine learning techniques, state-of-the-art tools, and generative AI workflows to elevate the team's analytical capability.

Desired Qualifications

  • Excellent communication skills and the ability to explain complex quantitative concepts to non-technical stakeholders are listed as an advantage.

About the Company

Riskified

eCommerce fraud prevention and revenue protection

Riskified helps online merchants protect revenue and prevent fraud in ecommerce by using machine-learning models to approve or block transactions in real time before they reach the bank. Its products include pre-authorization decisions and Dynamic Checkout that adjusts the checkout experience based on each shopper’s risk level, reducing friction for legitimate customers while flagging suspicious activity. The company differentiates itself with real-time, pre-authorization ML decisions that tie fraud protection directly to revenue growth and a checkout experience that adapts to risk, scalable for merchants expanding into new markets. Its goal is to help merchants grow revenue and improve customer satisfaction by preventing fraud, lowering false declines, and streamlining the digital checkout process.

Company size
501-1,000
Company stage
IPO
Headquarters
New York City, New York
Founded
2013

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