PayPal is seeking a Senior Machine Learning Engineer to build advanced fraud prediction models to prevent identity, onboarding, authentication, abuse, and scam-related fraud, thereby enhancing the security and reliability of their global financial platform.
Requirements
- Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
- Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
- Several years of experience in designing, implementing, and deploying machine learning models.
- Leverage anomaly detection, supervised learning, and experiential learning techniques to create robust and effective fraud prevention solutions.
- Apply supervised learning techniques to build predictive models that accurately identify fraudulent activities.
- Utilize continual learning methods to continuously improve model performance and adapt to new fraud tactics.
- Ensure data integrity and consistency by working closely with business stakeholders and engineers to address critical data challenges.
Responsibilities
- Develop and optimize machine learning models for various applications.
- Preprocess and analyze large datasets to extract meaningful insights.
- Deploy ML solutions into production environments using appropriate tools and frameworks.
- Collaborate with cross-functional teams to integrate ML models into products and services.
- Monitor and evaluate the performance of deployed models.
- Design and implement core decision models for identity, onboarding, authentication, abuse, scam, product-specific models.
- Develop and refine algorithms for detecting anomalies and identifying potential fraud patterns.
Other
- Minimum of 5 years of relevant work experience and a Bachelor's degree or equivalent experience.
- Work closely with cross-functional teams, including tech, operations, and product teams, to integrate fraud prediction models into various systems and processes.
- Conduct experiments, analyze results, and interpret findings to drive innovation and enhance decision-making processes.
- Promote and maintain a data-driven culture by engaging with diverse internal teams and advocating for best practices in data science and fraud prevention.
- PayPal's balanced hybrid work model offers 3 days in the office for effective in-person collaboration and 2 days at your choice of either the PayPal office or your home workspace.
