Machine Learning Engineer

Machine Learning Engineer

This job will validate and develop machine learning models and algorithms to solve complex problems. You will work closely with senior engineers, data scientists, and product teams to enhance services through innovative AI/ML solutions. Your role will involve building scalable ML pipelines, ensuring data quality, and deploying models into production environments to drive business insights and improve customer experiences.

Essential 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.

Minimum Qualifications:

• 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.

Additional Responsibilities & Preferred Qualifications :

• 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
• Perform model and AI risk governance related activities in line with enterprise risk framework, to ensure PayPal's AI applications are compliant with ever evolving regulatory expectation such as Responsible AI. 

Qualifications

• 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