Intern Data Scientist 2027 AI & Data Analytics

Introduction Launch your career like an IBMer Every IBMer has a story. For many, it started with an internship. As an IBM intern, you won't just gain experience - you'll start thinking, working, and growing like an IBMer.

From day one, you'll contribute to real client projects across diverse industries, including analytics, AI adoption, generative AI, and agentic AI-enabled transformation, working alongside experienced IBMers who are invested in your success. You'll be challenged, supported, and inspired, often all in the same day. You'll develop technical expertise and consulting skills in a culture built on continuous learning, mentorship, and coaching.

High-performing interns have a clear pathway into IBM's Associate Program, launching careers at one of the world's most innovative technology and consulting companies. To give yourself the best opportunity for success, we advise applying only to roles that align with your skills and experience, rather than applying broadly across all entry-level positions. You'll receive a status update email for each application, so be sure to check your IBM Careers account regularly — it's the best way to get a centralized view of which roles you have active applications against.

Your

Role

And

Responsibilities

During your internship, you can build data science and AI skills by contributing to client projects that use statistics, machine learning, data science, GenAI, and agentic AI solution patterns. This role provides an opportunity to build a compelling portfolio, acquire new skills, gain insight into diverse industries, and contribute to client-ready analytical and AI solution components with guidance from IBM teams. At IBM, we prioritize continuous learning, skill development, and personal growth within a culture of coaching and mentorship.

As an intern, you'll strengthen technical, analytical, and consulting skills while learning responsible AI practices, and you could advance to our full-time Associates program based on results and performance. Work Experiences You Could Be Exposed To Mentored Analytical Support: Receive mentorship from data scientists, AI engineers, consultants, and technical mentors while applying analytical rigor, statistical methods, and responsible AI practices to client challenges. Data Science and AI Development: Develop skills in writing efficient, reusable code to prepare data, build features, test models, and contribute to GenAI, RAG, or agentic AI solution components.

Effective Communication: Assisting explaining analytical results, model behavior, assumptions, limitations, and recommendations to both technical and non-technical audiences. Tech-Driven Problem Solver: Use tools such as Python, SQL, notebooks, cloud platforms, APIs, data platforms, and AI-assisted coding tools to analyze data and help turn ideas into working analytical or AI assets. Preferred

Education

Bachelor's Degree Required Technical And Professional Expertise Currently pursuing a quantitative degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, AI/ML, Cognitive Science, or a related field. Strong interpersonal skills that enhance collaboration and relationship building, while also managing dynamic workloads in an agile environment. Have initiative and passion to actively seek new knowledge and improve skills while embracing a growth mindset to assimilate diverse viewpoints.

Demonstrate leadership experience and ability to communicate effectively through active listening; while also be willing to adapt and have a readiness to take ownership of tasks and challenges. Familiarity with programming and analysis tools such as Python, SQL, R, or similar. Willingness to travel as needed.

Preferred Technical And Professional

Experience

Demonstrate familiarity or interest in statistical analysis, machine learning, data mining, GenAI, RAG, or agent-based applications through internships, coursework, personal or academic projects, hackathons, and/or publications.

Experience

using machine learning, data science, or AI frameworks such as pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow,LangChain,LangGraph,LlamaIndex, MCP-based tooling, or similar tools is a plus. Familiarity with AI-assisted coding and developer tools such as GitHub Copilot, Codex, Claude Code, Cursor, or similar tools for coding, testing, debugging, documentation, and code review.