PayPal Senior Machine Learning Engineer 

France, Ile-de-France 

794704950

• Model Development: Design and implement core decision models for identity, onboarding, authentication, abuse, scam, product-specific models.
• Anomaly Detection: Develop and refine algorithms for detecting anomalies and identifying potential fraud patterns.
• Supervised Learning: Apply supervised learning techniques to build predictive models that accurately identify fraudulent activities.
• Continual Learning: Utilize continual learning methods to continuously improve model performance and adapt to new fraud tactics.
• Collaboration: Work closely with cross-functional teams, including tech, operations, and product teams, to integrate fraud prediction models into various systems and processes.
• Experimentation and Analysis: Conduct experiments, analyze results, and interpret findings to drive innovation and enhance decision-making processes.
• Data Integrity: Ensure data integrity and consistency by working closely with business stakeholders and engineers to address critical data challenges.
• Advocacy: Promote and maintain a data-driven culture by engaging with diverse internal teams and advocating for best practices in data science and fraud prevention.

• Education : Master's degree or PhD in Computer Science, Statistics, Data Science, Machine Learning, Artificial Intelligence, or a related quantitative field (STEM).
• Experience : 5+ years of experience within Data Science, ML Engineering, or AI Research roles, with demonstrated expertise in building and deploying real-world predictive models.
• Skills : Strong understanding of anomaly detection, supervised learning techniques, and experiential learning methods. Experience in fraud prevention is a plus.
• Communication : Strong interpersonal, written, and verbal communication skills, with experience collaborating across multiple business functions.

• Expertise : Familiarity with decision models for identity and authentication.
• Domain Knowledge : Experience in fraud prevention and detection.
• Instrumentation : Experience driving data instrumentation for experimentation and large-scale data collection.
• Real-time Systems : Familiarity with building systems that incorporate real-time feedback and continuous learning.
• Advanced Techniques : Knowledge of reinforcement learning, contextual bandits, sequence models, optimization, or graph mining.

Travel Percent:

The total compensation for this practice may include an annual performance bonus (or other incentive compensation, as applicable), equity, and medical, dental, vision, and other benefits. For more information, visit .

The US national annual pay range for this role is $159,500 to $236,500

Our Benefits:

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