Lead Data Scientist

You will be responsible for the following key tasks: - Teach Applied AI/ML: Design and deliver practical, project-based courses in AI/ML, covering topics such as Python for ML, Statistics, ML Algorithms, Deep Learning, NLP, CV, ML Ops, and GenAI. - Develop Industry-Relevant Curriculum: Help design and update the AI/ML curriculum to incorporate current industry tools, techniques, and best practices, drawing from your own professional experience and case studies. - Mentor Student Projects: Guide students through hands-on AI/ML projects, offering 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: Keep abreast of the latest AI/ML trends and technologies to bring that knowledge into the classroom. - Collaborate: Work closely with other industry-expert faculty and staff to create a unified and effective learning experience. - Assess Practical Skills: Develop and evaluate assignments, projects, and assessments focused on measuring practical application and understanding. You should possess the following qualifications and experience: - Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI/ML, or a related field (PhD valued but not required). - Minimum 4-8 years of direct, hands-on professional experience in the tech industry as an AI/ML Engineer, Data Scientist, Research Scientist, or similar role involving significant AI/ML development and deployment. - Demonstrable industry track record of building, training, and deploying machine learning models to solve real-world problems. - Strong grasp of core ML algorithms, including classical & deep learning, model evaluation, statistics, and current research/industry trends. - Passion for Teaching/Mentoring: Ability and enthusiasm for explaining complex technical topics and guiding others. Your required skills include: 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, Cloud platforms - AWS/GCP/Azure). - Knowledge of key AI areas (NLP, Computer Vision, Generative AI/LLMs). Communication: - Excellent ability to explain technical concepts clearly to students. - Strong mentoring skills. Collaboration: - Team-oriented approach. Passion: - Genuine interest in education and student success within an innovative, practical learning environment. Good-to-Have: - Prior teaching experience at the undergraduate or graduate level. - Familiarity with modern teaching methodologies and academic tools.,