PayPal is seeking to solve complex problems through machine learning models and algorithms to enhance services and improve customer experiences, specifically in the area of fraud detection and checkout experiences.
Requirements
- At least 3 years of relevant industry experience in machine learning or data engineering
- Proficient in one or more programming and scripting languages, such as Python, Java, Scala, and Unix/Linux shell scripting
- Solid understanding of machine learning concepts, algorithms, and techniques, with hands-on experience in developing, training, and deploying ML models
- Experience using popular ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Familiarity with major cloud platforms (GCP, AWS, or Azure)
- Proven experience designing, implementing, and deploying end-to-end machine learning solutions in production environments
- Extensive experience with big data technologies such as Hadoop, Apache Spark, HBase, and Kafka
Responsibilities
- Design and deploy scalable traditional and generative AI solutions and productize ML models that enhance PayPal's ability to provide a seamless customer experience
- Work with data scientists and backend engineers to solve complex problems with AI/ML
- Ensure high code quality, performance, and reliability through rigorous testing, code reviews, and adherence to software development best practices
- Drive innovation by researching and incorporating state-of-the-art machine learning techniques, tools, and frameworks into the platform
- Mentor team members, provide technical guidance, and foster a culture of collaboration, innovation, and continuous learning
- Build scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences
- Design, develop, and implement machine learning models and algorithms to solve complex problems
Other
- Highly effective at working in cross-functional groups and getting results in matrix organizations
- Bachelor’s degree in computer science, engineering, or a related field (or equivalent experience)
- 0- travel percentage, with a flexible work environment, including 3 days in the office and 2 days at home or in the office
- Commitment to diversity and inclusion, with a workplace that values acceptance and security for all employees
- Ability to work in a team environment and collaborate with others to achieve common goals
