Staff Machine Learning Engineer – R&D, Fraud Intelligence at PayPal

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Fraud Detection, Anomaly Detection, Supervised Learning, Data Integrity, Collaboration, Experimentation, Data Science, Continuous Learning, Communication, Real-time Systems, Reinforcement Learning, Contextual Bandits, Sequence Models, Optimization, Graph Mining

Industry

Software Development

Description

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. 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 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. 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. Subsidiary:

Responsibilities

Design and implement decision models for fraud detection and develop algorithms for anomaly detection. Collaborate with cross-functional teams to integrate models into systems and conduct experiments to enhance decision-making processes.