Data Scientist ( ML & AI)

Research and Innovation: Stay up-to-date with the latest advancements in machine learning, especially in the field of generative AI. Conduct research to identify potential applications of generative AI in solving real-world problems. Model Development: Design and implement machine learning algorithms and models, including generative models like GANs (Generative Adversarial Networks) or VAEs (Variational Autoencoders). Optimize and fine-tune models for specific tasks and datasets. Data Analysis: Collaborate with data scientists to analyze and preprocess datasets, identifying patterns and features relevant to the task. Perform exploratory data analysis to gain insights into the data. Experimentation and Evaluation: Set up rigorous experiments to test the performance of AI models. Evaluate models using appropriate metrics and statistical methods. Iterate models based on feedback and evaluation results. Problem Solving: Work closely with domain experts and stakeholders to understand complex problems and formulate them as machine learning tasks. Develop creative solutions to challenging problems, often involving novel approaches. Algorithm Development: Create and implement new machine learning algorithms or adapt existing ones for specific use cases. Optimize algorithms for efficiency and scalability. Documentation and Communication: Document research findings, methodologies, and model architectures for internal use and potential publication. Communicate findings and progress effectively to both technical and non-technical team members.

Strong Mathematical Foundation. In-depth knowledge of linear algebra, calculus, probability, and statistics. Understanding of optimization techniques used in machine learning. Proficiency in programming languages such as Python and libraries like TensorFlow, PyTorch, or sci-kit-learn. Ability to write clean, efficient, and maintainable code. Deep understanding of machine learning algorithms, including supervised and unsupervised learning. Experience with generative models and neural networks. Skills in data preprocessing, feature engineering, and data visualization. Familiarity with tools like pandas, NumPy, and data manipulation libraries. Ability to read and understand research papers in machine learning. Creativity and a knack for thinking outside the box. Proficiency in designing experiments and selecting appropriate evaluation metrics. Statistical analysis skills for interpreting experimental results. Strong written and verbal communication skills to convey complex ideas to team members and stakeholders. Ability to break down complex problems into manageable tasks and develop innovative solutions. Effective collaboration skills to work with cross-functional teams, including data scientists, engineers, and domain experts.