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Machine Learning and Data Analytics Instructor
2 weeks ago
About Us
The University of Niagara Falls Canada is dedicated to providing a world-class education that prepares students for leadership in the digital world.
Our Master of Data Analytics program is designed to develop big data professionals who can grow in this high-demand career.
As a member of our faculty team, you will be responsible for teaching advanced courses in data science, machine learning, and predictive, prescriptive, and descriptive analysis.
You will also mentor students to achieve career success.
Responsibilities
- Teach advanced courses in data science, machine learning, and predictive, prescriptive, and descriptive analysis.
- Mentor students to achieve career success.
- Design and deliver comprehensive and engaging lectures on data science topics, covering areas such as machine learning, statistical analysis, and data modeling techniques.
- Develop teaching plans, notes, and instructional materials to support effective learning.
- Develop, supervise, and grade assignments, exams, and student projects.
- Monitor student attendance, academic progress, and provide feedback on performance.
- Provide academic counseling and career mentorship to support student's growth and success.
- Actively participate in curriculum development and continuous improvement of course content.
- Collaborate with other faculty members on research initiatives and interdisciplinary projects.
Requirements
- PhD in Mathematics, Economics, Computer Science, or a related field.
- Strong knowledge of machine learning techniques and algorithms.
- Expertise in predictive, prescriptive, and descriptive analysis.
- Proficiency in at least two of the following software tools: SAS, R, SQL, Python.
- Strong understanding of data analytics and data modeling techniques.
- Previous teaching experience at the university level, with a track record of successfully engaging and inspiring students.
We Value Diversity
The University of Niagara Falls Canada is committed to equity, diversity, and inclusivity within its community.
We encourage applications from all qualified candidates and invite racialized persons / persons of color, women, Indigenous / Aboriginal People of North America, persons with disabilities, 2SLGBTQI+ persons, and others who may contribute to the further diversification of ideas to apply.
