Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc.) Solid knowledge of SQL and Python's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib, etc.) Understanding of model evaluation, data pre-processing More ❯
learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance. Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer). Prior experience in training and fine-tuning of Large Language Models (LLMs) and knowledge of AWS platform and tools or equivalent cloud experience. More ❯
/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience. Hands-on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer). Prior experience in training and fine-tuning of Large Language Models (LLMs) and knowledge of AWS platform and tools. Amazon is an equal More ❯
and Large Language Models, to detect anomalies across billions of events. You'll design and implement sophisticated anomaly detection algorithms, such as Isolation Forests, LSTM-based models, and Variational Autoencoders, tailored to our unique data streams. Creating robust evaluation frameworks and metrics to assess the performance of these algorithms will More ❯
and implement principled strategies for data optimization. Key job responsibilities A Data Scientist-II should have a reasonably good understanding of NLP models (e.g. LSTM, LLMs, other transformer based models) or CV models (e.g. CNN, AlexNet, ResNet, GANs, ViT) and know of ways to improve their performance using data. You More ❯
