Senior Data Scientist + Instructor - AI & ML

About Newton:

About the Role:

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.)

● 7+ 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.

classical & deep learning

● 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.

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