Senior Machine Learning Engineer at PayPal

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

08 Jan, 26

Salary

0.0

Posted On

10 Oct, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Data Processing, Model Deployment, Python, SQL, Cloud Platforms, Statistical Analysis, Data Preprocessing, Fraud Detection, AI/ML Algorithms, Data Pipelines, Communication, Mentoring, Data Management, Experimentation

Industry

Software Development

Description

Lead the development and optimization of advanced machine learning models. Oversee the preprocessing and analysis of large datasets. Deploy and maintain ML solutions in production environments. Collaborate with cross-functional teams to integrate ML models into products and services. Monitor and evaluate the performance of deployed models, making necessary adjustments. You will design projects end-to-end, architecting innovative and cloud-agnostic data solutions. Design and implement reliable & scalable AI/ML-powered solutions. Translate business problems in the financial domain into technical solutions that leverage machine learning. Design data processing pipelines to efficiently ingest, store, and prepare data for AI/ML training and simulation. Integrate AI/ML models into existing fintech applications and workflows. Document architectural decisions, design patterns and best practices. Design and develop machine learning and deep learning systems. Running machine learning/artificial intelligence tests and experiments. Implementing appropriate ML/AI algorithms. Study and transform data science prototypes. Research and implement appropriate ML/AI algorithms and tools. Develop machine learning/AI applications according to requirements. Select appropriate datasets and data representation methods. Run machine learning/AI tests and experiments. Perform statistical analysis and fine-tuning using test results. Train and retrain systems when necessary. Extend existing ML/AI libraries and frameworks. Minimum of 8 years of relevant work experience and a Bachelor's degree or equivalent experience. Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment. Experience with model evaluation frameworks and deployment pipelines. (MLFlow, etc.) Familiarity with A/B testing and experimentation methodologies Proficiency in SQL for data extraction and sampling, familiarity with BigQuery cloud platform, Jupyter Notebooks. Proficiency in Python, especially for data manipulation (pandas, numpy) and visualization (seaborn, matplotlib, plotly). Strong experience with machine learning libraries (scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch). Experience with data cleaning and data pre-processing methods. Experience handling categorical variables, scaling, and encoding techniques (e.g., One-Hot Encoding, Target Encoding). Strong understanding of statistics and probability Strong understanding of data preprocessing, outlier handling, and missing value treatment. Experience designing and implementing stratified sampling techniques. Familiarity with correlation analysis, scatterplot matrix generation, and feature interaction studies. Knowledge of class imbalance techniques and evaluation metrics Excellent communication and mentoring skills, with the ability to explain technical concepts to non-experts. Proactive in identifying potential project bottlenecks and providing solutions. Minimum of 8 years of relevant work experience and a bachelor's degree or equivalent experience. Extensive experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment. Strong understanding of data management principles and best practices. Experience designing and implementing data pipelines for AI/ML projects. Fraud Detection Knowledge - Experience working with fraud or anomaly detection models is highly preferred.

Responsibilities

Lead the development and optimization of advanced machine learning models while overseeing the preprocessing and analysis of large datasets. Collaborate with cross-functional teams to integrate ML models into products and services, and monitor the performance of deployed models.