Machine Learning Engineer

PayPal•San Jose, CA

1d•$187,741 - $270,500•Hybrid

About The Position

PayPal, Inc. seeks Machine Learning Engineer in San Jose, CA. Job Duties: Design, develop, implement, deploy, and monitor predictive models using machine learning techniques, including neural networks and tree-based models. Work with large volumes of volume of data, including extracting insights and manipulating big data covering a wide range of information. Collaborate with other ML scientists and engineers to formulate innovative solutions, experiment, and implement advanced ML techniques to solve business problems. Clearly and effectively communicate complex concepts, analysis insights, and modeling results through creative visualization to stakeholders of varying technical levels. Build credit models to predict delinquency, fraud and repayment behavior for merchant loans, conduct experiments to improve model performance and facilitate model deployment. Ensure credit models solve business problems, ensure models’ compliance to regulation, enhance internal risk controls and improve operational efficiency to enable the best user experience. Partial telecommuting permitted from a commutable distance.

Requirements

Master’s degree, or foreign equivalent, in Mathematics, Statistics, or a closely related field.
Develop and deploy machine learning models using Python, including NumPy and PyTorch frameworks.
Write and optimize SQL queries in Google BigQuery or HiveQL for high-volume data extraction, transformation, and feature engineering.
Implement deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers.
Apply natural language processing (NLP) techniques for text analytics and language understanding.
Conduct experimental design for model building and selection methods.
Perform statistical analysis including regression modeling and hypothesis testing.
Create analytical visualizations in Python and/or Excel to communicate data insights.
Programming on Unix command line and Bash scripting.
Use Git for source-code management.
Experience in Jupyter Notebooks and/or Google Colab for interactive analysis and modeling.

Responsibilities

Design, develop, implement, deploy, and monitor predictive models using machine learning techniques, including neural networks and tree-based models.
Work with large volumes of data, including extracting insights and manipulating big data covering a wide range of information.
Collaborate with other ML scientists and engineers to formulate innovative solutions, experiment, and implement advanced ML techniques to solve business problems.
Clearly and effectively communicate complex concepts, analysis insights, and modeling results through creative visualization to stakeholders of varying technical levels.
Build credit models to predict delinquency, fraud and repayment behavior for merchant loans.
Conduct experiments to improve model performance and facilitate model deployment.
Ensure credit models solve business problems, ensure models’ compliance to regulation, enhance internal risk controls and improve operational efficiency to enable the best user experience.