Data Scientist Eng.

Exora AI is looking for an Data Scientist Engineer who plays a crucial role in the development, implementation, and maintenance of artificial intelligence systems and solutions. Responsibilities: * Model Development: Develop and implement machine learning and deep learning models tailored to specific business needs and use cases. Collaborate with stakeholders to understand requirements and translate them into technical solutions. * Data Preprocessing: Collect, clean, and preprocess data from various sources to prepare it for model training. Handle missing values, outliers, and other data anomalies to ensure high data quality and reliability. * Model Training: Train and optimize machine learning models using appropriate algorithms and techniques. Fine\-tune hyperparameters, perform cross\-validation, and employ regularization methods to enhance model performance and generalization. * Evaluation and Validation: Evaluate model performance using relevant metrics and validation techniques. Conduct thorough analysis of model outputs and identify areas for improvement. Iterate on model design and parameters to achieve desired performance goals. * Deploy trained models into production environments and integrate them with existing systems and workflows. Implement scalable and efficient solutions for real\-time or batch inference, depending on application requirements. * Monitor deployed models for performance degradation, drift, and other issues. Implement monitoring solutions and automated alerts to detect anomalies and trigger retraining or model updates as needed. Maintain model documentation and version control for reproducibility and auditability. Requirements: * Education: Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Engineering, Mathematics, Statistics, or a related field. * Programming Skills: Proficiency in programming languages such as Python, R, or Julia. Experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, scikit\-learn, or Keras. * Machine Learning Expertise: Strong understanding of machine learning concepts, algorithms, and techniques. Experience with supervised and unsupervised learning, as well as deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). * Data Skills: Proficiency in data preprocessing, analysis, and visualization. Experience with data manipulation tools/libraries such as Pandas, NumPy, Matplotlib, or Seaborn. * Software Development: Familiarity with software engineering principles and best practices. Experience with version control systems (e.g., Git), agile methodologies, and collaborative development tools. * Problem\-Solving Abilities: Strong analytical and problem\-solving skills. Ability to understand complex problems, break them down into manageable components, and develop practical solutions. * Communication Skills: Excellent written and verbal communication skills. Ability to communicate technical concepts and results clearly and effectively to diverse audiences. * Teamwork and Collaboration: Proven ability to work effectively in cross\-functional teams. Willingness to collaborate with colleagues from diverse backgrounds and disciplines -