Data Science & Analytics Trainer/Faculty

Dice Academy Delhi

About the Role

Dice Academy is a specialized training institute focused on practical, job-ready skills. This full-time, on-site role in New Delhi involves delivering structured classroom training in data science to learners at various levels.

Key Responsibilities

• Deliver structured classroom training in data science, including statistics, machine learning, programming, and data visualization.
• Design and update curriculum.
• Prepare lesson plans.
• Create teaching materials.
• Conduct engaging lectures, labs, and project-based sessions.
• Assess learner performance through assignments, quizzes, and projects.
• Provide constructive feedback to learners.
• Mentor students on career paths and industry expectations.
• Collaborate with the academy team to maintain industry-aligned content.
• Integrate practical case studies.
• Participate in workshops or seminars as needed.

Requirements & Tech Stack

• Proficiency in core data science tools and languages, such as Python, R, SQL, and common ML libraries, is highly beneficial.
• Experience applying data science in real-world or industry projects, preferably with demonstrable case studies or portfolios.
• Strong communication and presentation skills, with an ability to engage diverse learner groups and adapt teaching styles.
• Familiarity with modern educational technologies, learning management systems, and online collaboration tools is an advantage.

Qualifications

• Strong Science Education and Mathematics skills, with the ability to explain complex quantitative concepts clearly.
• Solid grounding in Physics and related analytical subjects to support data-oriented problem-solving.
• Experience as a Qualified Teacher, including classroom management and learner evaluation.
• Effective Lesson Planning skills to design structured, outcome-focused sessions and projects.
• Bachelor’s or Master’s degree in a relevant field (such as Computer Science, Data Science, Engineering, Mathematics, or Statistics) is preferred.