Trainer in Data Science

Reporting To: General Manager - L&D

About the Role

EduSkills Foundation is looking to appoint an experienced and industry-oriented Data Science Trainer to deliver high-quality technical training to M.Tech students.

The trainer will be responsible for delivering structured classroom sessions, hands-on practical training, industry-oriented assignments, and capstone projects. The objective is to help students build strong foundations in data science while developing the practical skills required for research, higher studies, and industry careers.

Key Responsibilities

1. Data Science Training

Deliver comprehensive training covering:

• Python for Data Science
• Statistics & Probability for Data Science
• Data Analysis and Data Visualization
• NumPy and Pandas
• SQL & Database Fundamentals
• Exploratory Data Analysis (EDA)
• Machine Learning
• Supervised & Unsupervised Learning
• Feature Engineering
• Model Evaluation & Optimization
• Data Pre-processing
• Introduction to Deep Learning
• Natural Language Processing (NLP)
• Time Series Analysis
• Generative AI / LLM Fundamentals
• Responsible & Ethical AI

2. Hands-on Learning

The trainer will conduct practical sessions using tools and technologies such as:

• Python
• Jupyter Notebook / Google Colab
• Pandas
• NumPy
• Matplotlib / Seaborn
• Scikit-learn
• SQL
• TensorFlow / PyTorch
• Power BI / Tableau, where applicable
• Git/GitHub

3. Projects & Capstone Development

• Design and mentor real-world Data Science projects.
• Guide students in identifying problem statements and datasets.
• Support students in data collection, cleaning, analysis, modelling and evaluation.
• Mentor students in developing their M.Tech projects and capstone projects.
• Review project progress and provide technical feedback.
• Encourage students to work on research-oriented and industry-relevant use cases.

4. Assessments & Evaluation

• Prepare assignments, quizzes, coding exercises and practical assessments.
• Conduct periodic evaluations of student performance.
• Assess students based on both technical knowledge and practical implementation.
• Provide constructive feedback and identify areas requiring improvement.

5. Research & Industry Orientation

• Introduce students to current developments and emerging trends in Data Science and AI.
• Encourage research-oriented thinking and problem-solving.
• Discuss real-world industry use cases.
• Guide students on research papers, datasets, experimentation and technical documentation.
• Support students in developing portfolios, GitHub repositories and project presentations.

Required Technical Skills

The trainer should have strong practical knowledge of:

Programming

• Python

Data Analysis

• NumPy
• Pandas
• Data Cleaning
• Exploratory Data Analysis

Databases

• SQL
• Relational Databases

Machine Learning

• Scikit-learn
• Regression
• Classification
• Clustering
• Ensemble Methods
• Model Evaluation
• Feature Engineering

Data Visualisation

• Matplotlib
• Seaborn
• Power BI / Tableau – preferred

Advanced Skills – Preferred

• Deep Learning
• TensorFlow / PyTorch
• NLP
• Generative AI
• LLMs
• Computer Vision
• MLOps / Cloud Platforms

Educational Qualification

Preferred qualifications:

• M.Tech / M.E. / M.Sc. in Data Science, Artificial Intelligence, Computer Science, Information Technology, Statistics or a related field

A Ph.D. or research experience will be an added advantage.

Experience

Preferred: 3+ years of relevant experience in Data Science, Machine Learning, AI, Analytics, or technical training.

Candidates with strong industry experience and demonstrated expertise in Data Science may also be considered.

Prior experience in training M.Tech / postgraduate students will be an added advantage.

Trainer Expectations

The selected trainer should be:

• Strong in practical implementation
• Excellent in classroom communication
• Capable of explaining complex concepts in a simple manner
• Experienced in project mentoring
• Comfortable handling postgraduate-level students
• Industry-oriented and updated with current technologies
• Able to conduct coding and hands-on sessions
• Capable of delivering both theoretical and practical sessions
• Willing to mentor students beyond classroom delivery

Deliverables

The trainer will be expected to:

• Deliver the agreed Data Science curriculum.
• Conduct hands-on coding and laboratory sessions.
• Provide assignments and practical exercises.
• Conduct assessments.
• Mentor student projects.
• Track student learning progress.
• Provide feedback and performance reports.
• Support students in developing industry-ready portfolios.
• Participate in project reviews and presentations.
• Maintain training quality and agreed academic timelines.

The final engagement model, duration, training hours and remuneration will be mutually agreed based on the programme requirements and trainer experience.

Pay: ₹300,000.00 - ₹900,000.00 per year

Benefits:

• Health insurance

Work Location: In person