Instructor – Data Analytics & AI

Job Description: Instructor – Data Analytics & AI

Company: DAIT Institute
Location: Sambhaji Nagar (Aurangabad), Maharashtra
Engagement: Full-time / Part-time / Visiting Faculty / Industry Mentor

Work Experience - 7 to 8 Years
Department: Education & Technology
Programme: AI-Led Data Analytics & AI

About DAIT Institute

DAIT Institute, a Group initiative with an education legacy dating back to 1998, is focused on bridging the gap between education and employment.

Our programmes combine industry-aligned curriculum, practical learning, AI-enabled tools, real-world projects, and mentorship to help students develop relevant skills for evolving technology careers.

We are looking for an experienced Data Analytics & AI professional who is passionate about teaching, mentoring, and preparing the next generation of technology professionals.

Role Overview

As an Instructor – Data Analytics & AI, you will deliver practical, industry-oriented training to graduates and working professionals. You will help learners understand data analytics concepts, apply analytical techniques to real-world problems, build projects, and develop the skills required for entry-level data and analytics roles.

The ideal candidate combines strong technical expertise with the ability to explain complex concepts clearly and guide learners through hands-on assignments and projects.

Key Responsibilities

1. Training & Instruction

• Deliver instructor-led sessions on Data Analytics, Business Intelligence, and foundational AI concepts.
• Teach Python for data analysis, SQL, Excel, statistics, and data visualization.
• Train students in Power BI or Tableau and dashboard development.
• Introduce machine learning fundamentals and practical AI applications relevant to analytics.
• Integrate appropriate AI tools into data analysis, problem-solving, and learning workflows.

2. Practical Learning & Projects

• Design hands-on exercises, assignments, case studies, and capstone projects.
• Help students work with real-world or appropriately anonymized datasets.
• Guide learners in data cleaning, exploratory data analysis, visualization, interpretation, and insight generation.
• Review project submissions and provide constructive, actionable feedback.
• Help students build a project portfolio demonstrating their analytical skills.

3. Student Mentoring & Assessment

• Evaluate student progress through assignments, quizzes, practical assessments, and projects.
• Identify learning gaps and provide additional guidance where needed.
• Support students in developing analytical thinking, problem-solving, and professional communication.
• Conduct project reviews, doubt-clearing sessions, and mock interviews where appropriate.
• Maintain attendance, assessment results, project evaluations, and progress updates

4. Curriculum & Industry Alignment

• Contribute to curriculum development and periodic updates.
• Keep training aligned with current industry practices and relevant tools.
• Use practical business scenarios from domains such as IT, finance, healthcare, manufacturing, and marketing.
• Support masterclasses, workshops, college engagement programmes, and industry-oriented learning activities.

Technical Skills Required

Core skills

• SQL: querying, joins, aggregations, subqueries, and data manipulation.
• Python: Pandas, NumPy, data cleaning, analysis, and visualization.
• Excel: formulas, pivot tables, data analysis, and reporting.
• Statistics: descriptive statistics, probability, hypothesis testing, and correlation.
• Data visualization: Power BI or Tableau, dashboard design, and KPI reporting.

AI & advanced analytics

• Understanding of machine learning fundamentals and common use cases.
• Familiarity with AI-assisted analytics, generative AI tools, and responsible AI usage.
• Ability to demonstrate practical analytical workflows and explain model outputs.
• Familiarity with scikit-learn and basic model evaluation is an advantage.

Preferred additional skills

• Experience with real-world datasets and business intelligence projects.
• Knowledge of data storytelling, business metrics, and analytical problem-solving.
• Exposure to cloud-based data platforms, data engineering, or big-data tools.

Eligibility & Experience

• Bachelor’s or Master’s degree in Data Science, Computer Science, Information Technology, Statistics, Mathematics, Engineering, or a related discipline; equivalent demonstrable i…