About the company
PayPal has been revolutionizing commerce globally for more than 25 years. Creating innovative experiences that make moving money, selling, and shopping simple, personalized, and secure, PayPal empowers consumers and businesses in approximately 200 markets to join and thrive in the global economy. For more information, visit https://www.paypal.com ,  https://about.pypl.com and  https://investor.pypl.com. PayPal provides equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, pregnancy, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, PayPal will provide reasonable accommodations for qualified individuals with disabilities. If you are unable to submit an application because of incompatible assistive technology or a disability, please contact us at [email protected].
Job Summary
In your role as a machine learning scientist, you will:
đź“ŤDesign, develop, implement, deploy, and monitor predictive models using machine learning techniques, including neural networks and tree-based models. đź“ŤWork with large volumes of data, including extracting insights and manipulating big data covering a wide range of information. đź“ŤCollaborate with other ML scientists and engineers to formulate innovative solutions, experiment, and implement advanced ML techniques to solve business problems. đź“ŤClearly and effectively communicate complex concepts, analysis insights, and modeling results through creative visualization to stakeholders of varying technical levels.
Requirements:
đź“ŤMinimum Qualifications: đź“ŤPhD in quantitative science or engineering field (Computer Science, Statistics, Physics, Mathematics, Operation Research, etc.). đź“ŤStrong understanding of the foundation of machine learning algorithms and statistical methods, including bagging, boosting, neural network, etc. đź“ŤExcellent analytical. problem-solving abilities, communication skills, and a great team player đź“ŤPrevious research or practical experience in machine learning is a significant advantage. đź“ŤProficiency in languages used for querying, data processing, statistical analysis, model building, and monitoring, including Python, SQL (BigQuery/Hive/Spark), and bash.
