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

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

14 Jan, 26

Salary

0.0

Posted On

16 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Fraud Detection, Anomaly Detection, Supervised Learning, Data Integrity, Collaboration, Data Science, Predictive Modeling, Continuous Learning, Experimentation, Data Analysis, TensorFlow, Scikit-learn, Reinforcement Learning, Problem Solving, Communication

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. Assist in the development and optimization of machine learning models. Preprocess and analyze datasets to ensure data quality. Collaborate with senior engineers and data scientists on model deployment. Conduct experiments and run machine learning tests. Stay updated with the latest advancements in machine learning. Minimum of 2 years of relevant work experience and a Bachelor's degree or equivalent experience. Familiarity with ML frameworks like TensorFlow or scikit-learn. Strong analytical and problem-solving skills. 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. Education: Master's degree or PhD in Computer Science, Statistics, Data Science, Machine Learning, Artificial Intelligence, or a related quantitative field (STEM). Experience: 3+ 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.

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.