Associate Data Scientist 2027
IBM
New York, United StatesFresher1h agovia LinkedIn
This job at a glance
- Who can apply
- New grads and up to 1 year · Bachelor's degree
- Must know
- LLMsAgileData structures & algorithmsPythonRSQL
- Nice to have
- Machine learningStatistics
- Work
- On-site in New York
- Pay
- Not stated by the employer.
- Skill demand
- Python74%LLMs28%Data structures & algorithms25%Agile10%of 99 similar entry-level jobs posted in the US this week
- Listed
- Oct 29, 2026 · still open
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About the Role
IBM's Associate Program is designed for university hires to collaborate with global clients and IBM teams on projects across digital transformation, cloud strategy, AI adoption, process redesign, analytics, generative AI, and agentic AI-enabled transformation. This role is a strong fit for analytical problem solvers who want to use data science, machine learning, and GenAI/Agentic AI-enabled workflows responsibly to uncover patterns, evaluate intelligent systems, and help clients make better decisions.
Key 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 & Tech Stack
- 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
- 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.
- Preferred: Bachelor's degree in a related field such as Computer Science, Data Science, Statistics, Mathematics, MIS, Engineering, AI/ML, or another quantitative field.