10 of 10 Logistic Regression Jobs

Seaborn) - Statistical & Multivariate Analysis Big Data: - Hadoop, MapReduce, HDFS, DataBricks, Spark, PySpark - DataLake Architecture, AWS Redshift, Kinesis, EMR Machine Learning - Supervised Models: Naïve Bayes, Logistic Regression, SVM, Linear Regression, KNN - Tree-Based Models: Decision Trees, Random Forest, Gradient Boosted Trees, XGBoost - Unsupervised Models: K-Means, DBSCAN, Hierarchical ...

Seaborn) Statistical & Multivariate Analysis Big Data: Hadoop, MapReduce, HDFS, DataBricks, Spark, PySpark DataLake Architecture, AWS Redshift, Kinesis, EMR Machine Learning Supervised Models: Nave Bayes, Logistic Regression, SVM, Linear Regression, KNN, Tree-Based Models: Decision Trees, Random Forest, Gradient Boosted Trees, XGBoost Unsupervised Models: K-Means, DBSCAN, Hierarchical ...

model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian ...

model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian ...

model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian ...

model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian ...

model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian ...

model testing. Extensive Experience with at least three of the following statistical, econometric, data science, and predictive modeling approaches: Unsupervised Learning; K-Means; Linear Regression; Time-Series/Forecasting; Stress Testing; Logistic Regression; Gaussian Process; Simulation Models; Boosting/Bagging Trees; Neural Networks; Deep Learning Concepts; Bayesian ...

knowledge. Role: Architect & Build GenAI Systems: Design and deploy advanced RAG and GraphRAG applications ML Implementation: Develop and maintain traditional machine learning models (Regression, Classification, Clustering, Time-Series) to solve structured data problems Agentic Workflows: Build multi-agent systems using frameworks like LangGraph, AutoGen, or similar to automate complex … Experience: Experience: 3-5 years of professional engineering experience Core ML & Data Science: Proven experience applying traditional ML techniques (Random Forest, XGBoost, Linear/Logistic Regression) to real-world datasets Strong grasp of fundamentals: Transformers, Embeddings, Feature Engineering, and Model Evaluation Generative AI Stack: Deep hands-on experience ...

from "zero-shot" implementations to data-driven optimization. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimize user outcomes based on real feedback. Be a "Full Stack" Partner: While the backend/data is your ...