The AI & Data Analytics Trainer will deliver practical, industry-relevant training that equips learners with foundational and applied skills in data analytics, artificial intelligence and generative AI. The trainer will facilitate classroom and lab sessions, assess learner performance, provide mentoring and prepare learners for relevant entry-level employment and further learning opportunities.

Key Responsibilities

Training Delivery

- Deliver classroom, virtual and lab-based training according to the approved curriculum and session plans.

- Train learners in data collection, data cleaning, exploratory analysis, visualisation and interpretation.

- Deliver foundational concepts in artificial intelligence, machine learning and generative AI.

- Teach relevant tools and technologies, which may include:
- Microsoft Excel and advanced spreadsheet functions

- SQL and relational databases

- Python for data analysis

- Pandas, NumPy and basic statistical libraries

- Power BI or Tableau

- Jupyter Notebook or Google Colab

- Generative AI and productivity tools

- Explain statistics, data structures, algorithms and machine-learning concepts at a level appropriate to the learners.

- Demonstrate the use of AI tools for research, analysis, content development, coding assistance and workplace productivity.

- Use practical exercises, case studies, projects, demonstrations and problem-solving activities.

- Adapt delivery methods for learners with different educational backgrounds and levels of digital proficiency.

- Provide remedial and additional support to learners who require assistance.

Practical Projects and Labs

- Guide learners through end-to-end data-analysis projects using realistic datasets.

- Support learners in developing dashboards, reports, presentations and analytical portfolios.

- Conduct coding exercises and supervised lab sessions.

- Help learners translate business questions into appropriate analytical approaches.

- Review learner code, queries, dashboards and project documentation.





- Encourage learners to communicate findings through clear narratives and visualisations.

- Ensure that lab systems, software and learning resources are used responsibly.

Responsible AI and Data Practices

- Teach learners about data privacy, security, bias, fairness and responsible use of AI.

- Explain the limitations of AI-generated outputs, including hallucinations and inaccurate or misleading results.

- Train learners to verify AI-generated information and disclose the use of AI where appropriate.

- Ensure that learners do not upload confidential, personal or sensitive information to unauthorised AI platforms.

- Promote ethical data collection, analysis, interpretation and presentation.

- Reinforce academic integrity and prevent plagiarism or unacknowledged AI-generated submissions.

Learner Management and Mentoring

- Maintain a safe, inclusive and participative learning environment.

- Monitor learner attendance, punctuality, engagement and academic progress.

- Identify learning gaps and implement suitable remedial interventions.

- Counsel and motivate learners to improve retention and course completion.

- Support learners in developing communication, problem-solving, teamwork and workplace skills.

- Mentor learners on career pathways in AI, data analytics, business intelligence and related fields.

Assessment and Documentation

- Conduct diagnostic, formative, practical and final assessments.

- Evaluate assignments, coding exercises, dashboards, presentations and capstone projects.

- Provide timely and constructive feedback to learners.

- Maintain accurate attendance, assessment and learner-progress records.





- Prepare and submit training, performance and project reports within prescribed timelines.

- Maintain supporting documentation required for reviews, audits and programme reporting.

- Ensure assessments reflect both conceptual knowledge and practical application.

Placement and Career Support

- Prepare learners for technical assessments, interviews and workplace interactions.

- Conduct mock interviews and technical practice sessions.

- Guide learners in preparing resumes, project portfolios and qualified profiles.

- Coordinate with the placement team to assess and communicate learners’ job readiness.

- Provide placement recommendations based on demonstrated skills and performance.

- Engage with employers and industry professionals to understand changing skill requirements.

- Follow up with placed learners and provide support for workplace retention when required.

Curriculum and Quality Improvement

- Review training content and recommend updates based on industry developments.

- Develop or adapt lesson plans, datasets, practical exercises, quizzes and project briefs.

- Stay informed about developments in artificial intelligence, analytics and digital technologies.

- Participate in trainer-development programmes, reviews and standardisation exercises.

- Share best practices and learning resources with other trainers.

- Ensure compliance with NIIT Foundation’s quality, safeguarding and organisational standards.

Key Performance Indicators

- Training delivery against the approved plan.

- Learner attendance, retention and course-completion rates.

- Performance in theoretical and practical assessments.

- Completion and quality of learner projects and portfolios.

- Certification, internship and placement outcomes.

- Post-placement retention.

- Learner and employer feedback.

- Accuracy and timeliness of documentation and reports.

- Compliance with curriculum and programme-quality standards.

📌 Trainer (Jaunpur)
🏢 Niit Foundation
📍 Jaunpur