About the job
This job is responsible for independently validating and providing oversight of high-impact statistical, machine learning, and AI models across key business areas such as credit, fraud, marketing, and collections. It involves assessing model soundness, data quality, and performance to identify and mitigate model risk, while collaborating with developers and business partners on remediation and improvement. The position also supports AI and model risk governance to ensure compliance with PayPal’s enterprise risk framework and evolving regulatory standards.
Responsibilities
Develop and optimize machine learning models for various applications.
Preprocess and analyze large datasets to extract meaningful insights.
Deploy ML solutions into production environments using appropriate tools and frameworks.
Collaborate with cross-functional teams to integrate ML models into products and services.
Monitor and evaluate the performance of deployed models.
Qualifications
Minimum
3+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
Experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
Familiarity with cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
Several years of experience in designing, implementing, and deploying machine learning models.
Preferred
Advanced degree (Master's or Ph.D.) in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, or a related field.Strong knowledge of statistical and machine learning techniques, including but not limited to logistic regression, time-series modeling, random forests, support vector machines, gradient boosting (e.g., XGBoost), and deep learning architectures (e.g., CNNs, RNNs).Proficiency in programming and big-data technologies, with hands-on experience in tools such as Python (Scikit-learn, TensorFlow), SQL, Hadoop, and Spark.Relevant modeling experience in one or more of the following domains: credit risk scoring, fraud detection, financial forecasting, or marketing analytics - gained through industry or academic research.Strong collaboration and communication skills, with the ability to work effectively both independently and as part of a cross-functional team.Ability to articulate complex technical concepts clearly to non-technical stakeholders and build constructive working relationships across functions.Experience with Large Language Models (LLMs), Agentic AI, or related generative AI applications.Familiarity with model governance, model risk management, or AI regulatory compliance frameworks (e.g., SR 11-7, OCC 2011-12, EU AI Act) is a plus.