Data Scientist Intern: 2027

We are seeking enthusiastic and driven interns to join the AI, Automation, and Data Platforms (AADP) team at IBM CIO. As an intern, you will play a critical role in developing cutting-edge solutions using Watsonx LLMs (Large Language Models), Watsonx Orchestrate, Milvus, and other state-of-the-art technologies. You will work closely with cross-functional teams to integrate these solutions into business processes, orchestrate various components, and build scalable solutions leveraging automation, AI, and data technologies. This role requires a strong understanding of business needs and the ability to translate them into technical stories to guide development: Hands-on experience working with IBM's cutting-edge technologies, including GenAI (Watsonx.ai platform), LLM technologies, vector databases (Watsonx.data), and automation tools (Watson Orchestrate). Opportunities to enhance your programming skills, critical thinking, and problem-solving abilities. Exposure to real-world challenges in AI orchestration, automation, and business-driven development. Experience working in a dynamic, innovation-focused environment alongside leading experts in AI, automation, and data platforms. Networking opportunities within IBM's global business and technology ecosystem. Role Overview: As a Data Scientist intern in the AI, Automation and Data Platform organization, you will be in a unique position to combine your strategic thinking with your technical skills in AI, machine learning, and data analytics. You will apply your skills to help implement data-driven solutions that align with business goals. You will steer enterprise projects that improve decision-making, solve complex problems, and drive business growth. This role involves working with team members and stakeholders to translate data insights into actionable recommendations that deliver meaningful business impact. Key Responsibilities: AI, Data Science, and Technical Execution: Support the design, implementation and optimization of AI-driven strategies per business stakeholder requirements. Design and implement machine learning solutions and statistical models, from problem formulation through deployment, to analyze complex datasets and generate actionable insights. Apply GenAI, traditional AI, ML, NLP, computer vision, or predictive analytics where applicable. Collect, clean, and preprocess structured and unstructured datasets. Help refine data-driven methodologies for transformation projects. Learn and utilize cloud platforms to ensure the scalability of AI solutions. Leverage reusable assets and apply IBM standards for data science and development. Apply ML Ops and AI ethics. Strategic Planning Translate business requirements into technical strategies. Ensure alignment to stakeholders’ strategic direction and tactical needs. Apply business acumen to analyze business problems and develop solutions. Collaborate with stakeholders and team to prioritize work. Project Management and Delivering Business Outcomes: Manage and contribute to various stages of AI and data science projects, from data exploration to model development to solution implementation and deployment. Use agile strategies to manage and execute work. Monitor project timelines and help resolve technical challenges. Design and implement measurement frameworks to benchmark AI solutions, quantifying business impact through KPIs. Communication and Collaboration: Communicate regularly and present findings to collaborators and stakeholders, including technical and non-technical audiences. Create compelling data visualizations and dashboards. Work with data engineers, software developers, and other team members to integrate AI solutions into existing systems. Required Skills and Qualifications Pursuing a Bachelor’s degree in Computer Science, Data Science, Statistics, Economics, or a related field. Experience with AI/ML technologies and statistical modeling through coursework, projects, or past internships or full time positions. Technical Skills: Proficiency in SQL and Python for performing data analysis and developing machine learning models. Experience and/or coursework in statistics, machine learning, generative and traditional AI. Knowledge of common machine learning algorithms and frameworks: linear regression, decision trees, random forests, gradient boosting (e.g., XGBoost, LightGBM), neural networks, and deep learning frameworks such as TensorFlow and PyTorch. Familiarity with cloud-based platforms and data processing frameworks. Understanding of large language models (LLMs). Familiarity with object-oriented programming. Experience and/or coursework with common Python libraries used by data scientists (e.g., NumPy, Pandas, SciPy, scikit-learn, matplotlib, Seaborn, etc.) Strategic and Analytical Skills: Strategic thinking and business acumen. Strong problem-solving abilities and eagerness to learn. Ability to work with datasets and derive insights. Attention to detail. Communications and Soft Skills: Excellent communication skills, with the ability to explain technical concepts clearly. Independent and team-oriented. Understands AI Ethics principles. United States Data & Analytics Hybrid Internship Armonk, US Armonk (0147) International Business Machines Corporation Analytical Skills Artificial Intelligence Communication Computer Science Data Analysis Data Processing Data Science Deep Learning Economics Ethics Machine Learning Object-Oriented Programming Python Structured Query Language Soft Skills Statistical Modeling Statistics Business Acumen Detail-oriented Strategic Thinking Decision Trees Neural Networks NumPy Pandas Library PyTorch SciPy Scikit-learn Tensorflow Large Language Model Technical Proficiency Boosting Matplotlib Seaborn Language Models Linear Regression Machine Learning Algorithms XGBoost Library