Generative AI and Data Science Intern

This is a remote position.

The program is divided into three levels: beginner, intermediate, and advanced. Each level covers a range of topics, from the basics of data science and AI to more advanced concepts such as machine learning, deep learning, and generative AI.

Accepted Participants will learn based on a self-study approach, carry out tasks and complete projects in line with a well-structured and curated Internship Program Curriculum and resources which we provide to you at no cost, their progress will be monitored and supervised throughout the cohort to ensure that you remain on track and accountable. During the program, you will learn alongside innovative and inspiring leaders through mentorship and gain valuable experience working on real-life solutions while also acquiring some work-ready, soft-skills along the way.

NOTE: This is not a PAID opportunity therefore, payment as stipends to cover data allowance and other needs are not available for participants at this time. Every participant is expected to have a working laptop and access to data to allow FULL participation including progress tracking at different times throughout the cohort.

Requirements

Familiarity or Working knowledge in at least one programming language such as Python and its associated libraries such as TensorFlow, PyTorch, or scikit-learnis required.

Familiarity or Working knowledge of backend development principles, frameworks and technologies is essential. This includes experience with server-side programming languages such as Python, Java, or Node.js and frameworks such as Flask or Django, as well as proficiency in working with databases and RESTful APIs.

Familiarity or Understanding of fundamental AI concepts and techniques such as machine learning, deep learning, neural networks and architectures, data modeling, computer vision, and NLP (Natural Language Processing).

Familiarity with Data Manipulation and Analysis: Proficiency in data manipulation and analysis, applicants should have basic knowledge of large datasets, performing data preprocessing, feature engineering and data visualization using visualization tools such as Google Data Studio, Power BI, Tableau, e.t.c.

Familiarity with AI Ethics and Bias.

Accountable via set metrics and performance indicators

Analytical and problem-solving skills.

Communication Skills and Teamwork.

Portfolio or Demonstrable Projects that demonstrate your skills and passion for AI development. This can include personal projects, academic projects, or contributions to open-source AI projects would be an advantage for participants joining at the Level 3 (Advanced stage).

NOTE: Applications submitted at this time will ONLY be taken into consideration towards the September to December (2025) cohort of the program. However, intending participants whose SIWES or NYSC placement begin after September 2025 or extend beyond December 2025 are encouraged to apply and communicate their dates accordingly.

Benefits

Mentorship from Industry Experts: Gain valuable insights and guidance from industry-leading AI and Data Science professionals who will provide mentorship during the program.

Hands-on Projects: Apply your learning to real-world projects, providing you with practical experience and a strong portfolio.

Access to Tools: Explore the latest AI and Data Science tools and technologies, including generative AI frameworks, to stay ahead of the curve.

Career Advancement: Prepare for a successful career in AI and Data Science with our comprehensive program, opening doors to exciting opportunities.

Networking Opportunities: Connect with a diverse and supportive community of fellow learners and professionals in the field.

Desired Skills and Experience

Familiarity or Working knowledge in at least one programming language such as Python and its associated libraries such as TensorFlow, PyTorch, or scikit-learnis required.

Familiarity or Working knowledge of backend development principles, frameworks and technologies is essential. This includes experience with server-side programming languages such as Python, Java, or Node.js and frameworks such as Flask or Django, as well as proficiency in working with databases and RESTful APIs.

Familiarity or Understanding of fundamental AI concepts and techniques such as machine learning, deep learning, neural networks and architectures, data modeling, computer vision, and NLP (Natural Language Processing).

Familiarity with Data Manipulation and Analysis: Proficiency in data manipulation and analysis, applicants should have basic knowledge of large datasets, performing data preprocessing, feature engineering and data visualization using visualization tools such as Google Data Studio, Power BI, Tableau, e.t.c.

Familiarity with AI Ethics and Bias.

Accountable via set metrics and performance indicators

Analytical and problem-solving skills.

Communication Skills and Teamwork.

Industries:Computer Software

Function: Data Science