Description

Algorithm Development: Design, develop, and implement machine learning algorithms and models to solve business problems and optimize processes. Collaborate with cross-functional teams to identify opportunities for AI-driven solutions. Data Collection and Preparation: Collect, preprocess, and analyze large volumes of structured and unstructured data from various sources. Clean, normalize, and transform data to prepare it for training and validation. Model Training and Evaluation: Train and fine-tune machine learning models using state-of-the-art algorithms and techniques. Evaluate model performance using appropriate metrics and validation methods. Iterate on model design and hyperparameters to optimize performance. Deep Learning: Develop deep learning models using frameworks such as TensorFlow, PyTorch, or Keras. Implement convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures for image recognition, natural language processing, and other tasks. Feature Engineering: Extract and engineer meaningful features from raw data to improve model performance and generalization. Use domain knowledge and statistical techniques to select relevant features and reduce dimensionality. Role: Data Science & Machine Learning - Other Industry Type: Financial Services Department: Data Science & Analytics Employment Type: Full Time, Permanent Role Category: Data Science & Machine Learning

Role and Responsibilities

• Algorithm Development: Design, develop, and implement machine learning algorithms and models to solve business problems and optimize processes. Collaborate with cross-functional teams to identify opportunities for AI-driven solutions. Data Collection and Preparation: Collect, preprocess, and analyze large volumes of structured and unstructured data from various sources. Clean, normalize, and transform data to prepare it for training and validation. Model Training and Evaluation: Train and fine-tune machine learning models using state-of-the-art algorithms and techniques. Evaluate model performance using appropriate metrics and validation methods. Iterate on model design and hyperparameters to optimize performance. Deep Learning: Develop deep learning models using frameworks such as TensorFlow, PyTorch, or Keras. Implement convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning architectures for image recognition, natural language processing, and other tasks. Feature Engineering: Extract and engineer meaningful features from raw data to improve model performance and generalization. Use domain knowledge and statistical techniques to select relevant features and reduce dimensionality. Role: Data Science & Machine Learning - Other Industry Type: Financial Services Department: Data Science & Analytics Employment Type: Full Time, Permanent Role Category: Data Science & Machine Learning