Role & responsibilities

1. Curriculum Development:
   Design and develop the data science curriculum, ensuring it is up-to-date with current industry trends and technologies.

• Create lesson plans, assignments, projects, and assessments that align with educational standards and learning objectives.

Instruction:

• Teach data science concepts, tools, and techniques to students, which may include statistics, machine learning, data visualization, and programming languages like Python and R.
• Use a variety of instructional methods to accommodate different learning styles and keep students engaged.

Practical Applications:

• Develop and supervise lab sessions, coding exercises, and practical projects where students can apply data science methods to real-world problems.
• Guide students in the use of data science software and tools

Mentoring and Support:

• • Offer academic advising and support to students, helping them with coursework, projects, and career guidance in data science.
  • Mentor students in research projects, internships, and other extracurricular activities related to data science.

1. Professional Development:

• Stay current with developments in the field of data science through continuous learning and professional development.
• Attend workshops, conferences, and training sessions to enhance teaching skills and knowledge of data science advancements.

The given responsibilities are designed to ensure that students gain a comprehensive and practical understanding of data science, preparing them for future careers in this rapidly evolving field.

Preferred candidate profile

• Educational Background:
  • A Masters or Doctorate degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
  • Relevant teaching certification or credentials (e.g., PGT certification for high school level).
• Experience:
  • Proven experience in teaching data science or related subjects at the high school, college, or university level.
  • Industry experience in data science, analytics, or a related field is a plus.

Skills: Technical Proficiency:

• • Strong knowledge of programming languages commonly used in data science, such as Python, R, SQL, and possibly others like Java or Scala.
  • Proficiency in data analysis, statistical methods, machine learning, and data visualization tools and techniques.
  • Experience with data science software and tools