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 ❯
passion for building scientifically driven solutions in a fast-paced environment. This Senior Applied Scientist should have a good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. They should leverage exceptional technical expertise More ❯
services such as SageMaker, Bedrock, EMR, S3, OpenSearch Service, Step Functions, Lambda, and EC2. Hands-on experience with deep learning techniques (e.g., CNN, RNN, LSTM, Transformer), machine learning, CV, GNN, or distributed training. Excellent communication skills, with the ability to explain complex mathematical concepts to non-experts. Amazon is an More ❯
or equivalent practical experience. Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g., deep learning, load shedding and traffic management (LSTM), convolutional networks). Experience in architecting and developing software or infrastructure for scalable, distributed systems. Experience in data and information management as it relates to More ❯
