5 of 5 Random Forest Jobs in the UK

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 Clustering Deep Learning - ANN, CNN, RNN, LSTM - Frameworks: TensorFlow (Gradient Tape), PyTorch ...

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 Clustering Deep Learning ANN, CNN, RNN, LSTM Frameworks: TensorFlow (Gradient Tape), PyTorch ...

Deep understanding of ML algorithms (supervised/unsupervised learning, reinforcement learning) Extensive experience with neural networks, including RNNs, LSTMs, Transformers Expertise in gradient boosting, random forests, and ensemble methods Experience with generative models (GANs, VAEs, Diffusion models) Programming & Tools : Expert-level Python programming Proficiency with ML frameworks (PyTorch, TensorFlow ...

Deep understanding of ML algorithms (supervised/unsupervised learning, reinforcement learning) Extensive experience with neural networks, including RNNs, LSTMs, Transformers Expertise in gradient boosting, random forests, and ensemble methods Experience with generative models (GANs, VAEs, Diffusion models) Programming & Tools : Expert-level Python programming Proficiency with ML frameworks (PyTorch, TensorFlow ...

Chroma) and ML model registries 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 ...