Data Science Trainer

Roles & Responsibilities

• Deliver instructor-led classroom, online, and hybrid training sessions in Data Science and related technologies.
• Train students on Python, Statistics, Machine Learning, Deep Learning, SQL, Data Visualization, and AI concepts.
• Design and update course content, lesson plans, assignments, and practical lab exercises based on current industry trends.
• Conduct hands-on training using real-world datasets and industry-oriented projects.
• Mentor students in capstone projects, case studies, and portfolio development.
• Evaluate student performance through assessments, quizzes, assignments, and mock interviews.
• Provide career guidance, resume reviews, and interview preparation for students.
• Support the placement team by conducting technical mock interviews and skill assessments.
• Stay updated with the latest developments in Data Science, Artificial Intelligence, Machine Learning, Generative AI, and data analytics tools.
• Create technical documentation, presentations, and learning resources.
• Assist the business development and marketing teams by conducting technical seminars, webinars, workshops, and demo sessions.
• Collaborate with curriculum designers to continuously improve training programs.
• Ensure high student engagement, satisfaction, and course completion rates.
• Maintain attendance records, training reports, student feedback, and academic documentation.
• Guide students on best coding practices, model development, deployment, and problem-solving techniques.

Technical Skills Required

• Strong knowledge of Python programming.
• Statistics and Probability.
• Machine Learning algorithms using Scikit-learn.
• Deep Learning using TensorFlow and PyTorch.
• Data Analysis using Pandas and NumPy.
• Data Visualization using Matplotlib, Seaborn, Plotly, and Power BI/Tableau.
• SQL and database management.
• Feature Engineering and Model Evaluation.
• Natural Language Processing (NLP).
• Generative AI concepts and LLMs (e.g., OpenAI APIs, LangChain, Retrieval-Augmented Generation).
• Git/GitHub for version control.
• Jupyter Notebook and Google Colab.
• Basic knowledge of cloud platforms such as AWS, Azure, or Google Cloud is an advantage.