Associate Data Scientist 2027 - AI & Data Analytics

About the role

Join IBM's Associate Program for university hires and collaborate with global clients and IBM teams on projects that help organizations solve tough challenges across digital transformation, cloud strategy, AI adoption, process redesign, analytics, generative AI, and agentic AI-enabled transformation. As an Associate, you will work alongside a global cohort of diverse, ambitious peers and have access to industry-recognized certifications, digital badges, and a minimum of 40 hours of structured learning per year on IBM's AI-driven learning platform, supported by coaches, mentors, and professional communities across practices. IBM's culture of internal mobility allows you to explore new technologies, industries, and career paths as your interests evolve.

Responsibilities

• Prepare, cleanse, join, and analyze structured, semi-structured, and unstructured data for analytics, machine learning, GenAI, and agentic AI workflows.
• Implement and validate predictive, prescriptive, statistical, and machine learning models with a focus on measurable client and business outcomes.
• Design experiments, features, prompts, evaluation datasets, metrics, and tests to assess model outputs, workflow accuracy, reliability, bias, and business value.
• Build GenAI, RAG, and agentic AI workflows by evaluating retrieval quality, grounding, generated responses, tool-use outcomes, and end-to-end agent performance.
• Work in an Agile, collaborative environment with data scientists, data engineers, AI engineers, consultants, architects, and database administrators to bring analytical rigor and responsible AI practices to client challenges.
• Communicate with internal and external clients to understand business needs, select appropriate modeling techniques, and explain assumptions, limitations, and recommendations.
• Evaluate modeling results and present insights clearly to technical and non-technical audiences, translating findings into practical actions.

Requirements

• Strong fundamentals in mathematics, statistics, computer science, algorithms, and analytical reasoning.
• Familiarity with programming languages such as Python, SQL, R, or similar.
• Foundational understanding of predictive modeling, prescriptive modeling, statistical methods, machine learning, GenAI, and Agentic AI.
• Basic understanding of cloud environments, data platforms, or AI platforms such as AWS, Azure, Google Cloud, or similar.
• Strong technical and analytical abilities, problem-solving, debugging, troubleshooting, teamwork, and communication skills.
• Willingness to travel up to 100%, based on project requirements.

Qualifications

• Bachelor's degree in a related field such as Computer Science, Data Science, Statistics, Mathematics, MIS, Engineering, AI/ML, or another quantitative field.
• Coursework, projects, internship experience, or portfolio work involving data science, machine learning, analytics, GenAI, and Agentic AI.
• Familiarity with LLMs, embeddings, vector databases, retrieval systems, prompt workflows, RAG, model behavior evaluation, and AI agent workflow patterns.
• Exposure to orchestration and agentic AI frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, MCP-based tooling, or similar tools.
• Understanding of Git, containers, APIs, testing frameworks, CI/CD, MLOps, LLMOps, observability, model monitoring, data governance, privacy, security, or responsible AI practices.