Data Science and AI Intern - RTP 2027

Introduction At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. Are you ready to lead in this new era of technology and solve some of the world's most challenging problems? If so, lets talk

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We are seeking a motivate Intern to join our AI and Data Science development team. In this role, you will contribute to the design, development, and implementation of AI solutions, while also supporting the integration and performance of these systems. You will also look for ways to effienciently collect, clean, analyze, and visualize data to support business decisions that support real-world applications across enterprise systems. You’ll work closely with engineering, support, and senior developers to ensure AI models are robust, scalable, and aligned with business needs; and helping to create predictive models, generate insights and help optimize company performance. This is a hands-on technical role ideal for someone passionate about AI and eager to grow their skills in both development and systems-level implementation. Required Technical And Professional Expertise • Programming Proficiency: Solid understanding of Python and/or another programming language commonly used in AI (e.g., Java, C++, or R). • Mathematics & Statistics: Foundational knowledge in linear algebra, calculus, probability, and statistics. • Machine Learning Basics: Familiarity with core ML concepts such as supervised and unsupervised learning, model evaluation, and overfitting. • Data Handling: Experience working with data using libraries like NumPy, pandas, or similar. • Version Control: Basic understanding of Git and collaborative development workflows. • Communication: Ability to clearly articulate technical concepts and collaborate effectively in a team environment. Preferred Technical And Professional Experience • Deep Learning Frameworks: Exposure to TensorFlow, PyTorch, or JAX. • AI Project Experience: Prior coursework, personal projects, or research involving AI/ML. • Cloud Platforms: Familiarity with cloud services like AWS, Azure, or Google Cloud for AI workloads. • Software Engineering Practices: Understanding of testing, debugging, and code optimization. • Visualization Tools: Experience with tools like Matplotlib, Seaborn, or Plotly for data visualization. • Knowledge of AI Ethics: Awareness of fairness, bias, and responsible AI development practices.