Data Scientist

Qualification 1. A Bachelor's degree in a related field (Computer Science, Statistics, Mathematics, Engineering, etc.). A Master's or Ph.D. is a plus. 2. Minimum of 3.5 years of hands-on experience as a Data Scientist, with a strong focus on time-series data analysis, classification techniques, and experience in training machine learning models using a variety of techniques, including supervised, unsupervised, and reinforcement learning. 3. Profound knowledge of supervised learning algorithms (e.g., regression, decision trees, support vector machines, neural networks) and unsupervised learning techniques (e.g., clustering, dimensionality reduction). 4. Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) and experience in developing deep neural networks for classification tasks. 5. Demonstrated ability to design, build, and optimize machine learning models for real-world applications. 6. Ability to work with large and complex datasets. 7. A systematic approach... to experimentation, including A/B testing and cross-validation. 8. Willingness to work in a fast-paced start-up environment. 9. Effective communication skills to collaborate with cross-functional teams, present findings, and explain complex machine learning concepts to non-technical stakeholders. Nice to have 1. Working knowledge of pattern recognition and signal processing using Data analysis. 2. Experience with cloud and software technologies 3. Working experience with MLOps. What will we do together? As a Data Scientist in our team, we will collaborate to: 1. Leverage Machine Learning Techniques: Work together to apply a wide range of machine learning methods, including supervised, unsupervised, and reinforcement learning, to solve complex problems and drive business outcomes. 2. Model Development: Collaborate in the development of cutting-edge machine learning models, with a focus on classification tasks, from concept to deployment. 3. Data Exploration and Preprocessing: Explore and preprocess data to create high-quality datasets, ensuring that our models are trained on the most relevant and reliable information. 4. Evaluation and Optimization: Continuously evaluate model performance and optimize models for accuracy, efficiency, and scalability. 5. Innovation: Encourage innovation and experimentation to push the boundaries of what machine learning can achieve within our organization. 6. Cross-Functional Collaboration: Collaborate with diverse teams across the company, sharing insights and ensuring alignment with business objectives. 7. Professional Development: Support your ongoing professional development and growth in the field of machine learning and data science,

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