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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. We are seeking a talented Senior Machine Learning Engineer to join our team and focus on building advanced fraud prediction models. This role involves developing core decision models for various aspects of fraud prevention, including identity, onboarding, authentication, abuse, scam, and product-specific models. The ideal candidate will leverage anomaly detection, supervised learning, and experiential learning techniques to create robust and effective fraud prevention solutions. Responsibilities
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. Continuous 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. Requirements
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. Preferred Qualifications
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. The base pay for this role will depend on where you work and the relevant experience and expertise you bring. The expected range of pay for this role by location is: Primary Location | Pay Range:
Chicago, Illinois | ($117,500.00 - $174,350.00 Annually) Additional Location(s) | Pay Range:
San Jose, California | ($129,500.00 - $191,950.00 Annually) Austin, Texas | ($117,500.00 - $174,350.00 Annually) Scottsdale, Arizona | ($111,500.00 - $165,550.00 Annually) Omaha, Nebraska | ($111,500.00 - $165,550.00 Annually) Additional compensation for this role may include an annual performance bonus, equity, or other incentive compensation, as applicable. PayPal does not charge candidates any fees for courses, applications, resume reviews, interviews, background checks, or onboarding. When making an application directly, we will never ask you to share passwords, one-time passcodes (OTP), or verification codes. Any such request is a red flag and likely part of a scam. All communication regarding your application will come from official PayPal email domains. If you suspect fraudulent activity, please report it immediately. To learn more about how to identify and avoid recruitment fraud please visit https://careers.pypl.com/contact-us. For the majority of employees, 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, ensuring that you equally have the benefits and conveniences of both locations. At PayPal, we're committed to building an equitable and inclusive global economy. And we can't do this without our most important asset-you. That's why we offer comprehensive, choice-based programs, to support all aspects of personal wellbeingphysical, emotional, and financialdelivering meaningful value where it matters most. We strive to create a flexible, balanced work culture with a holistic approach to benefits, including generous paid time off, healthcare coverage for you and your family, and resources to create financial security and support your mental health. Our employees are central to advancing our mission, and we strive to create an environment where everyone can do their best work with a sense of purpose and belonging. Belonging at PayPal means creating a workplace with a sense of acceptance and security where all employees feel included and valued. We are proud to have a diverse workforce reflective of the merchants, consumers, and communities that we serve, and we continue to take tangible actions to cultivate inclusivity and belonging at PayPal. Any general requests for consideration of your skills, please join our Talent Community.
Sprachkenntnisse
- English
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