Teaching Assistant - Data Science & Data Analyst Program

Role Overview: As a Teaching Assistant for the Data Science and Data Analyst Offline Programs, you will be a crucial support system for learners as they progress from foundational analytics to cutting-edge technologies such as Deep Learning, Natural Language Processing (NLP), and Generative AI. Your role will involve conducting doubt-clearing sessions, guiding hands-on activities, assisting in project development, and creating an engaging and application-driven learning environment in collaboration with instructors. Key Responsibilities: - Conduct regular doubt-clearing sessions covering core topics such as Python, statistics, SQL, Excel, data visualization, machine learning, deep learning, NLP, and generative AI. - Organize hands-on exercises, case studies, and model-building challenges using real-world datasets and tools like Pandas, Scikit-learn, Tensorflow, PyTorch, Hugging Face, and OpenAI APIs. - Monitor learner progress, identify conceptual gaps, and provide personalized support across foundational and advanced AI concepts. - Foster an engaging classroom environment by promoting curiosity, experimentation, and collaboration. Sub-Goals: - Strengthen learners" understanding in analytical and AI-driven topics through practice-oriented sessions. - Help students build confidence in solving complex problems using classical and modern AI tools. Outcome-Oriented Learning Facilitation: - Collaborate with instructors to assist learners in achieving key milestones such as Exploratory Data Analysis, Dashboard Design, Machine Learning Projects, NLP Pipelines, and Generative AI Capstone Projects. - Review and provide feedback on assignments involving regression, classification, neural networks, transformers, chatbot building, and generative model applications. - Guide learners in developing a diverse portfolio showcasing skills in data analysis, business problem-solving, and AI innovation. - Ensure students gain hands-on exposure to AI applications like image generation, language modeling, and real-time analytics. Sub-Goals: - Equip students with practical, in-demand skills for roles such as Data Analyst, Business Analyst, Data Scientist, and AI Engineer. - Bridge theoretical knowledge with practical implementation using industry-adopted tools and frameworks. Batch Management, Coordination, and Continuous Improvement: - Maintain batch records, track attendance, learner progress, and project completion. - Coordinate with academic and operation teams for seamless batch execution, including scheduling expert sessions on AI and managing resource distribution. - Resolve operational or technical issues hindering the learning process. - Collaborate in refining teaching methods and integrating feedback for improved delivery of advanced AI concepts. Sub-Goals: - Ensure smooth logistics and academic execution across beginner and advanced AI learning tracks. - Actively contribute to enhancing AI curriculum effectiveness and learner engagement strategies. Qualification Required: - Education: Bachelor's degree in Computer Science, Engineering, Statistics, or related field (or equivalent industry experience). - Technical Skills: Proficiency in Python, SQL, Excel, Statistics, data visualization, machine learning, deep learning frameworks, NLP libraries, and generative AI tools. - Hands-on Experience: Developing and deploying ML/DL models, building dashboards, NLP pipelines, and working with generative models. - Communication: Ability to explain fundamental and advanced AI concepts clearly and engagingly. - Mentorship: Passion for teaching and mentoring learners at various stages of their data and AI journey. - Collaboration & Time Management: Balancing academic guidance, operational tasks, and team collaboration effectively.,