Data Scientist + Instructor

Key Responsibilities: ● Teach Applied AI/ML: Design and deliver practical, project-based courses in AI/ML (Python for ML, Statistics, ML Algorithms, Deep Learning, NLP, CV, ML Ops, GenAI). ● Develop Industry-Relevant Curriculum: Help design and update the AI/ML curriculum to reflect current industry tools, techniques, and best practices, incorporating your professional experience and case studies. ● Mentor Student Projects: Guide students through hands-on AI/ML projects, providing technical direction, code reviews, and feedback based on industry standards. ● Guide & Mentor Students: Advise students on developing practical skills, understanding career paths in AI/ML, and preparing for internships and job placements. ● Stay Current: Bring the latest AI/ML research, tools, and industry trends into the classroom. ● Collaborate: Work closely with other expert faculty and staff to create a unified and effective learning experience. ● Assess Practical Skills: Design and evaluate assignments, projects, and assessments focused on real-world applications.

Qualifications and Requirements: ● Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI/ML, or related field. (PhD is valued but not mandatory.) ● 6+ years of direct, hands-on professional experience in the tech industry as an AI/ML Engineer, Data Scientist, Research Scientist, or similar role involving AI/ML development and deployment. ● Proven Industry Track Record: Demonstrated experience in building, training, and deploying machine learning models (including deep learning) for real-world problems. ● Deep AI/ML Understanding: Strong grasp of core ML algorithms (classical & deep learning – CNNs, RNNs, Transformers), model evaluation, statistics, and awareness of current research/industry trends. ● Passion for Teaching/Mentoring: Ability to explain complex concepts clearly and guide others. Prior mentoring, corporate training, technical workshops, or project supervision experience is highly relevant. (Formal academic teaching experience is not mandatory.) Required Skills: ● Technical: Expert-level Python programming. ● Proficiency with data science libraries (Pandas, NumPy, Scikit-learn). ● Hands-on experience with ML/DL frameworks (TensorFlow, PyTorch). ● Strong SQL and data handling skills. ● Understanding of ML Ops practices and tools (Git, Docker, AWS/GCP/Azure). ● Knowledge of key AI areas (NLP, Computer Vision, Generative AI/LLMs). ● Soft Skills: Strong communication, mentoring ability, collaboration, and a genuine passion for education. Good-to-Have: ● Prior teaching experience at the undergraduate or graduate level. ● Familiarity with modern teaching methodologies and academic tools.