Sr Machine Learning Engineer at PayPal

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

26 Feb, 26

Salary

0.0

Posted On

28 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Analysis, Model Deployment, PySpark, Credit Risk Modeling, Statistical Modeling, Deep Learning, Python, SQL, TensorFlow, Keras, Cloud Platforms, MLOps, Model Monitoring, Feature Engineering, Data Privacy

Industry

Software Development

Description

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. Design, develop, and operationalize scalable machine learning and credit risk models, leveraging PySpark and advanced modeling frameworks. Build, train, and optimize statistical and deep learning models focused on credit scoring, fraud detection, and portfolio risk management. Collaborate cross-functionally with credit risk analysts, data engineers, and business teams to translate analytical requirements into production-grade credit modeling solutions. Ensure model robustness, accuracy, and compliance through rigorous validation, back-testing, and performance monitoring. Develop and maintain automated ML pipelines for data ingestion, feature engineering, model training, and deployment across large-scale credit datasets. Implement and enhance MLOps practices, including model integration, model monitoring. Research and experiment with innovative modeling techniques (e.g., gradient boosting, neural networks, graph-based learning) to improve credit decisioning capabilities. Mentor junior team members, conduct peer code reviews, and promote engineering excellence within the credit modeling domain. 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. Bachelor's or Master's degree in Computer Science, Statistics, or a related quantitative field. 6+ years of experience in machine learning model development and deployment, preferably in the financial services or credit risk domain. Strong programming proficiency in Python and SQL, with hands-on experience using PySpark for large-scale data processing. Deep understanding of ML frameworks such as TensorFlow, Keras, or PyTorch. Expertise in distributed computing, scalable data pipelines, and model optimization techniques. Proven experience deploying models in production cloud environments (AWS, GCP, or Azure). Demonstrated ability to write clean, well-documented, and production-ready code. Experience in credit risk modeling. Understanding of model governance frameworks, ML explainability (e.g., SHAP, LIME), and regulatory compliance. Familiarity with feature store architectures, model drift detection, and automated model retraining workflows. Knowledge of data privacy and compliance practices relevant to credit data.

Responsibilities

Develop and optimize machine learning models for various applications and deploy ML solutions into production environments. Collaborate with cross-functional teams to integrate ML models into products and services while monitoring and evaluating their performance.