Data Scientist-Advanced Analytics

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success.

You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

• Develop and deploy machine learning, deep learning, and generative AI solutions.
• Design and implement agentic AI systems using frameworks such as Lang Graph, Auto Gen, CrewAI, Semantic Kernel, or similar platforms.
• Build and orchestrate multi-agent workflows, integrating AI agents with enterprise applications, APIs, and data sources.
• Develop RAG (Retrieval-Augmented Generation) solutions using vector databases, embeddings, and enterprise knowledge repositories.
• Fine-tune, evaluate, and optimize foundation models and LLM-based applications.
• Programming
  
  Languages:
  
  Exposure to programming languages, particularly Python, and development environments like PyCharm, VS Code, and Jupyter Notebooks

None

Generative & Agentic AI

• LLMs (GPT, Claude, Llama, Mistral, etc.)
• Prompt Engineering and RAG
• Agentic AI frameworks (Lang Graph, CrewAI, Auto Gen, Semantic Kernel)
• Multi-agent orchestration and workflow design
• MCP (Model Context Protocol) and agent integration patterns
• AI evaluation, observability, and governance

Traditional AI & Data Science

• Machine Learning and Deep Learning
• Statistical Modeling and Predictive Analytics
• NLP, Classification, Clustering, Time Series Forecasting
• Feature Engineering and Model Optimization
• Python (Pandas, Num Py, Scikit-learn, PyTorch, Tensor Flow)

Data & Cloud Technologies

• SQL, No
  
  SQL, Vector Databases
• Data Engineering and ETL/ELT concepts
• Azure AI, Databricks, AWS, or GCP AI Services
• MLOps / LLMOps, CI/CD, Containerization