Hypothesis Testing TTest ZTest Regression (Linear Logistic) Python/PySpark SAS/SPSS Statistical analysis and computing Probabilistic Graph Models Great Expectation Evidently AI Forecasting (Exponential Smoothing ARIMA ARIMAX) Tools(KubeFlow BentoML) Classification (Decision Trees SVM) ML Frameworks (TensorFlow PyTorch SciKit Learn CNTK Keras MXNet) Distance (Hamming Distance Euclidean Distance Manhattan Distance) R/ R Studio
Specialization
Data Science Advanced: AI/ML Engineer
Job requirements
Drift Frame Work : Framework for detecting drift Automatically monitor track accuracy and trigger model retraining and notifications to restore previous accuracy levels ML Generalist: Data Scientist with MLOPS Development and maintenance of ML pipeline ML Engineer focusing on experimentation and tracking Responsibilities: Model Development: Develop machine learning models and algorithms to solve business problems leveraging techniques such as supervised learning unsupervised learning and deep learning. Deployment and Integration: Deploy machine learning models into production environments and integrate them with existing systems and workflows. Performance Optimization: Optimize machine learning models for scalability efficiency and performance considering factors such as latency throughput and resource utilization. Monitoring and Maintenance: Monitor model performance in production identify and diagnose issues and implement solutions to ensure continued reliability and effectiveness. Collaboration: Collaborate with crossfunctional teams including data scientists software engineers and product managers to understand business requirements and deliver solutions that meet stakeholders needs. Research and Innovation: Stay uptodate with the latest advancements in artificial intelligence and machine learning research and explore new techniques and methodologies to improve model performance and capabilities.
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