Intern Data Scientist 2027 – AI & Data Analytics is a role where you will contribute to client projects involving analytics, generative AI, and agentic AI-enabled transformation. You will work on developing data science and AI components by applying statistical methods, machine learning, and responsible AI practices to solve diverse industry challenges. Day-to-day tasks include writing efficient code to prepare data, building features, testing models, and communicating technical results to both technical and non-technical audiences. The role requires proficiency in Python, SQL, R, and notebooks, alongside experience with cloud platforms, APIs, and AI-assisted coding tools like GitHub Copilot or Cursor. You will also utilize frameworks such as pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, LangChain, LangGraph, and LlamaIndex to build robust analytical assets and solve complex problems within a consulting environment.
What does a Data Scientist earn?
Median $162025 from 276 postings across 61 companies.
Contribute to client projects involving analytics, generative AI, and agentic AI-enabled transformations.
Develop reusable code to prepare data, build features, and test machine learning models.
Build components for GenAI, RAG, or agentic AI solutions under the guidance of IBM teams.
Apply statistical methods and responsible AI practices to solve complex client challenges.
Use Python, SQL, cloud platforms, and APIs to transform ideas into working analytical assets.
Explain technical results, model behaviors, and recommendations to both technical and non-technical audiences.
Utilize AI-assisted coding tools for testing, debugging, and documentation of software components.
What we're looking for
High School Diploma or GED required.
Bachelor's Degree (preferred).
Currently pursuing a quantitative degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, AI/ML, Cognitive Science, or a related field.
Familiarity with programming and analysis tools such as Python, SQL, R, or similar.
Strong interpersonal skills to collaborate, build relationships, and manage dynamic workloads in an agile environment.
Demonstrated leadership experience, active listening skills, and a willingness to take ownership of tasks.
Experience with machine learning, data science, or AI frameworks like pandas, NumPy, SciPy, scikit-learn, PyTorch, TensorFlow, LangChain, LangGraph, LlamaIndex, or similar (preferred).
Familiarity or interest in statistical analysis, machine learning, data mining, GenAI, RAG, or agent-based applications through projects or coursework (preferred).