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, visitāÆhttps://www.paypal.com ,āÆ https://about.pypl.comāÆand āÆhttps://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
Essential Responsibilities:
šLead the development and implementation of advanced data science models. šCollaborate with stakeholders to understand requirements. šDrive best practices in data science. šEnsure data quality and integrity in all processes. šMentor and guide junior data scientists. šStay updated with the latest trends in data science.
Minimum Qualifications:
šMinimum of 5 years of relevant work experience and a Bachelor's degree or equivalent experience.
Preferred Qualification:
šStrong proficiency in Python and SQL for data analysis, machine learning, and automation. šSolid understanding of supervised and unsupervised AI/machine learning methods (e.g., XGBoost, LightGBM, Random Forest, clustering, isolation forests, autoencoders, neural networks, transformer-based architectures). šExperience in payment fraud, AML, KYC, or broader risk modeling within fintech or financial institutions. šExperience developing and deploying ML models in production using frameworks such as scikit-learn, TensorFlow, PyTorch, or similar.
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