Machine Learning Engineer

PayPal • Austin, TX

8d

About The Position

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. Support the lead of the team in performing oversight of high-impact statistical model and AI applications in a variety of business function areas, including but not limited to fraud detection, credit underwriting, marketing analytics etc. Conduct quantitative and qualitative model validation according to Model Risk Management Policy to identify and understand model risk issues Collaborate with business units and model developers to remediate model issues and provide subject-matter expert opinion on model improvements 1+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience. Familiarity with ML frameworks like TensorFlow or scikit-learn. Strong analytical and problem-solving skills. This position requires the ability and curiosity to learn various advanced modeling methods/AI techniques, covering a broader business function. This role also requires candidate to have the capability in building effective relationship with various stakeholders including business owners, model owners, model developers and control officers. The candidate must possess excellent communication, writing and presentation skills. An advanced degree in a quantitative field, such as statistics, mathematics, computer science or engineering essential Advanced knowledge of statistical and machine learning models (e.g., logistic regression, time series analysis, random forests, SVMs, XGBoost, CNNs/RNNs) Possessing advanced coding skills in dealing with big data (e.g., Scikit-learn in Python, Tensorflow, Hadoop, Spark , SQL, etc.) Relevant Modeling experience in credit scoring, fraud detection, financial forecasting, or marketing analytics obtained either in academic or financial industry Ability to work effectively both independently and in a team environment Ability to communicate effectively and establish constructive relationship with stakeholders

Requirements

• 1+ years relevant experience and a Bachelor's degree OR Any equivalent combination of education and experience.
• Familiarity with ML frameworks like TensorFlow or scikit-learn.
• Strong analytical and problem-solving skills.
• This position requires the ability and curiosity to learn various advanced modeling methods/AI techniques, covering a broader business function.
• This role also requires candidate to have the capability in building effective relationship with various stakeholders including business owners, model owners, model developers and control officers.
• The candidate must possess excellent communication, writing and presentation skills.
• An advanced degree in a quantitative field, such as statistics, mathematics, computer science or engineering essential
• Advanced knowledge of statistical and machine learning models (e.g., logistic regression, time series analysis, random forests, SVMs, XGBoost, CNNs/RNNs)
• Possessing advanced coding skills in dealing with big data (e.g., Scikit-learn in Python, Tensorflow, Hadoop, Spark , SQL, etc.)
• Relevant Modeling experience in credit scoring, fraud detection, financial forecasting, or marketing analytics obtained either in academic or financial industry
• Ability to work effectively both independently and in a team environment
• Ability to communicate effectively and establish constructive relationship with stakeholders

Responsibilities

• 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.
• Support the lead of the team in performing oversight of high-impact statistical model and AI applications in a variety of business function areas, including but not limited to fraud detection, credit underwriting, marketing analytics etc.
• Conduct quantitative and qualitative model validation according to Model Risk Management Policy to identify and understand model risk issues
• Collaborate with business units and model developers to remediate model issues and provide subject-matter expert opinion on model improvements

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level