Teaching Assistant - Data Science & Data Analyst Program

Teaching Assistant - Data Science & Data Analyst Program

We are seeking dedicated Teaching Assistants (TAs) for our Data Science and Data Analyst Offline Programs. As a TA, you will play a vital role in supporting learners throughout their journey–from

foundational analytics to cutting-edge technologies like Deep Learning, Natural Language Processing (NLP), and Generative AI. You will work closely with instructors to conduct doubt-clearing sessions,guide hands-on activities, support project development, and create a

vibrant, application-driven learning environment.

Key Responsibilities

1. Learner Support and Assistance

• Conduct regular doubt-clearing sessions covering core topics such as Python, statistics, SQL, Excel, data visualization (Tableau, Power BI), and advanced subjects like machine learning, deep learning, NLP, and generative AI.
• Organize structured hands-on exercises, data-centric 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 offer personalized support across foundational and advanced AI concepts.
• Foster an engaging classroom environment by encouraging curiosity, experimentation, and collaboration.

Sub-Goals:

• Strengthen learner’s understanding in both analytical and AI-driven topics through practice-oriented sessions.
• Help students build confidence in handling complex problems using classical and modern AI tools.

2. Outcome-Oriented Learning Facilitation

• Collaborate with instructors to support learners in achieving key milestones such as Exploratory Data Analysis, Dashboard Design, Machine Learning Projects, Deep Learning Models, 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 strong and diverse portfolio demonstrating 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 tools and frameworks widely adopted in the industry.

3. Batch Management, Coordination, and Continuous Improvement

• Maintain batch records, including attendance, learner progress, and project completion tracking.
• Coordinate with academic and operation teams for seamless batch execution, including scheduling expert sessions on AI and managing resource distribution.
• Resolve day-to-day operational or technical issues that hinder the learning process.
• Collaborate in continuously refining teaching methods, integrating feedback for better delivery of advanced AI concepts.

Sub-Goals:

• Ensure smooth logistics and academic execution across both beginner and advanced AI learning tracks.
• Contribute actively to improving AI curriculum effectiveness and learner engagement strategies.

Required Qualifications and Skills

• Education: Bachelor’s degree in Computer Science, Engineering, Statistics, or related field (or equivalent industry experience).
• Technical Skills: Strong foundation in Python, SQL, Excel, Statistics, data visualization (PowerBI, Tableau), and machine learning. Proficiency in deep learning frameworks (Tensorflow, PyTorch), NLP libraries (spaCy, Hugging Face), and generative AI tools (OpenAI, Diffusers, LangChain).
• Hands-on Experience: Experience in developing and deploying ML/DL models, building dashboards, NLP pipelines, and experimenting with generative models (text-to-image, language generation, etc.).
• Communication: Ability to clearly explain both fundamental and advanced AI concepts in an engaging and digestible way.
• Mentorship: Passion for teaching and mentoring learners across various stages of their data and AI Journey.
• Collaboration & Time Management: Able to balance academic guidance, operational tasks, and team collaboration effectively.

Preferred Qualifications

• Prior experience in mentoring learners in data fields.
• Project or teaching experience in deep learning, NLP applications (like sentiment analysis, chatbot building), or generative AI (like image generation, LLMs).
• Familiarity with collaborative coding (Git/GitHub), cloud-based notebooks (Google Colab), and APIs for deploying AI solutions.
• Participation in AI hackathons, case competitions, or publication of models/projects on platforms like Kaggle or Hugging Face Hub.