Intern Data Scientist 2027 – AI & Data Analytics is an internship role where you will contribute to client projects involving analytics, generative AI, and agentic AI-enabled transformation. You will work with data scientists and engineers to develop skills in statistics, machine learning, and responsible AI practices while building components for RAG or agent-based applications. Day-to-day tasks include writing reusable code to prepare data, build features, and test models, as well as communicating technical results to diverse audiences. The role requires proficiency in Python, SQL, R, and notebooks, along with experience in cloud platforms, APIs, and AI-assisted coding tools like GitHub Copilot or LangChain. You will solve complex business problems by transforming ideas into working analytical assets using frameworks such as pandas, NumPy, SciPy, scikit-learn, PyTorch, and TensorFlow.
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, and agentic AI solution patterns.
Apply statistical methods and responsible AI practices to solve complex client challenges.
Use Python, SQL, cloud platforms, and APIs to analyze data and create technical assets.
Explain analytical results, model behaviors, and recommendations to both technical and non-technical audiences.
Utilize AI-assisted coding tools for testing, debugging, and documentation of code.
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 the ability 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 with AI-assisted coding tools such as GitHub Copilot, Codex, Claude Code, Cursor, or similar (preferred).