Job Specifications
Introduction
About The Client Innovation Center (CIC)
IBM Consulting Client Innovation Centers (CICs) are high-delivery, team-based environments where technologists work onsite to build real solutions for real clients.
At CIC, interns collaborate closely with data scientists, data engineers, and consultants to support analytics and AI-enabled solutions across industries. Our delivery centers are built for learning through delivery, combining hands-on project exposure, structured training, mentorship, and teamwork to help students build strong analytical foundations and prepare for early-career roles in data and consulting.
This role is ideal for individuals who enjoy working with data, learning analytical techniques, and collaborating in an in-person, team-based environment.
Your Role And Responsibilities
The Data Scientist Intern role is a developmental, learning-focused position that provides hands-on exposure to data analysis, machine learning concepts, and analytics delivery in a professional consulting environment.
This role is not about owning models, designing enterprise AI systems, or being an expert on day one. It is about applying academic knowledge, learning how analytics is used to solve business problems, and supporting delivery teams with data-driven insights.
High-performing interns may be considered for conversion into IBM Consulting’s full-time Associate Data Scientist program based on performance and business needs.
As a Data Scientist Intern, You Will
Support data analysis and modeling activities under the guidance of experienced data scientists and consultants
Assist with gathering, cleaning, and preparing data for analysis and modeling
Contribute to exploratory data analysis and basic statistical or machine learning techniques
Help evaluate model results and document findings
Support the development of simple data pipelines or analytical workflows using programming languages such as Python
Assist in communicating analytical insights to technical and non-technical team members
Learn how analytics, AI, and automation are applied within client delivery environments
Build analytical and professional skills through mentorship, training, and hands-on project work
Work Location & Onsite Expectations:
This internship role is based in an IBM Client Innovation Center (CIC) and requires regular onsite presence at the assigned delivery center. Working onsite enables hands-on learning, real-time collaboration, and mentorship that support faster growth and stronger delivery outcomes. Interns work in a collaborative, in-person team environment.
Work Authorization
IBM will not provide visa sponsorship for this position. Candidates must be eligible to work in the U.S. without current or future sponsorship requirements.
Preferred Education
Bachelor's Degree
Required Technical And Professional Expertise
These qualifications are essential for success in the role.
Analytical and Learning Skills
Strong interest in data science, analytics, machine learning, or AI
Analytical and problem-solving skills, with the ability to approach tasks using structured, logical thinking
Comfortable working onsite in a collaborative, team-based environment
Ability to work effectively in a technology-driven consulting environment where tools, platforms, and client needs evolve over time
Ability to learn new tools, techniques, and technologies in a delivery setting
Technical Foundation Skills
Currently pursuing coursework in a quantitative or technical field such as Computer Science, Data Science, Mathematics, Statistics, Engineering, AI, or a related discipline
Coursework or hands-on experience using Python or another analytical programming language
Familiarity with basic concepts such as data manipulation, statistics, or algorithms through coursework, labs, or projects
Education
High School Diploma or GED
Preferred
Currently pursuing a Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, AI, or a related field.
Preferred Technical And Professional Experience
Preferred Skills (Nice-to-Haves):
Exposure to data analysis, statistical modeling, or machine learning through academic projects, internships, hackathons, or research
Familiarity with common data science libraries (e.g., pandas, NumPy, scikit-learn, or similar)
Exposure to cloud or analytics platforms through coursework or labs
Interest in areas such as AI-enabled analytics, automation, or data-driven decision making
Experience working on team-based projects that involve data analysis or technical problem-solving