
DataScience Faculty
3 days ago
Role Overview
We are seeking a Data Science Faculty / Trainer with a proven blend of industry execution (IT or service-based projects) and academic delivery (EdTech or corporate training).
You will be responsible for delivering end-to-end learning programs in Data Science, Machine Learning, AI, and Generative AI, while also integrating real-world IT project experience into your sessions.
The ideal candidate is both a hands-on practitioner and an educator — capable of mentoring learners, translating enterprise-grade solutions into simple concepts, and driving measurable learning outcomes.
Key Responsibilities
- Deliver instructor-led sessions on:
- Python for Data Science
- Statistics & Probability
- Exploratory Data Analysis (EDA)
- Machine Learning (Supervised & Unsupervised)
- Deep Learning (ANN, CNN, RNN, LSTM)
- Natural Language Processing (NLP)
- SQL, Power BI, Tableau
- Cloud AI tools (AWS, Azure, GCP optional)
- Design, develop, and update course content, assignments, and capstone projects.
- Conduct hands-on lab sessions and case study-driven workshops.
- Mentor students in projects and guide them through end-to-end ML lifecycle.
- Evaluate students’ progress through quizzes, assignments, and viva sessions.
- Stay updated with current industry trends, tools, and frameworks in AI/ML.
- Support placement readiness through technical mock interviews and portfolio reviews.
- Collaborate with academic teams to enhance learning delivery and student experience.
Required Skills and Competencies
- Proficiency in Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn.
- Strong knowledge of Statistics, Probability, Linear Algebra, and Data Modeling.
- Experience with Machine Learning algorithms (Regression, Classification, Clustering, PCA, etc.).
- Familiarity with Deep Learning frameworks – TensorFlow, Keras, PyTorch (preferred).
- Exposure to NLP, Computer Vision, and Generative AI is a plus.
- Hands-on with SQL, Power BI / Tableau for visualization.
- Familiarity with Jupyter Notebook, Git, and cloud ML environments.
- Excellent communication and presentation skills.
- Passion for teaching, mentoring, and simplifying complex topics.
Qualification
- Bachelor’s or Master’s Degree in Computer Science / Data Science / AI / Statistics / Mathematics / Engineering.
- Professional certifications (e.g., IBM Data Science, Google ML, AWS ML, or Coursera Specializations) are a plus.
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