Data Scientist-Advanced Analytics

A career at IBM Consulting offers the opportunity to work closely with leading companies worldwide, helping them navigate their hybrid cloud and AI transformation journeys. Supported by IBM’s advanced technology, strategic partnerships, and Red Hat, you will contribute to driving meaningful change and accelerating client impact. The culture at IBM Consulting encourages curiosity, innovation, and continuous growth, fostering an environment where your unique skills and experiences are valued and your long-term career development is prioritized.

Key Responsibilities
- Develop and deploy machine learning, deep learning, and generative AI solutions tailored to client needs.
- Design and implement agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar platforms.
- Build and orchestrate multi-agent workflows, integrating AI agents with enterprise applications, APIs, and diverse data sources.
- Develop Retrieval-Augmented Generation (RAG) solutions leveraging vector databases, embeddings, and enterprise knowledge repositories.
- Fine-tune, evaluate, and optimize foundation models and large language model (LLM)-based applications to ensure high performance.
- Utilize programming languages, particularly Python, within development environments like PyCharm, VS Code, and Jupyter Notebooks to build and test AI models.

Required Qualifications
- Strong expertise in Generative and Agentic AI, including familiarity with LLMs such as GPT, Claude, Llama, and Mistral.
- Proficiency in prompt engineering and RAG methodologies.
- Experience with agentic AI frameworks including LangGraph, CrewAI, AutoGen, and Semantic Kernel.
- Ability to design and orchestrate multi-agent workflows and understand MCP (Model Context Protocol) and agent integration patterns.
- Knowledge of AI evaluation, observability, and governance practices to maintain model integrity and compliance.
- Solid foundation in traditional AI and data science techniques, including machine learning, deep learning, statistical modeling, and predictive analytics.
- Hands-on experience with NLP tasks such as classification, clustering, and time series forecasting.
- Skilled in feature engineering and model optimization.
- Proficiency in Python and relevant libraries such as Pandas, NumPy, Scikit-learn, PyTorch, and TensorFlow.

Preferred Qualifications and Benefits
- Familiarity with data and cloud technologies including SQL, NoSQL, and vector databases.
- Understanding of data engineering principles and ETL/ELT processes.
- Experience with cloud AI services from providers such as Azure AI, Databricks, AWS, or Google Cloud Platform.
- Knowledge of MLOps and LLMOps practices, including CI/CD pipelines and containerization technologies.

This role does not specify formal educational requirements, emphasizing skills and practical experience instead. Joining IBM Consulting means becoming part of a collaborative and innovative team dedicated to pushing the boundaries of AI and cloud technology to deliver transformative solutions for clients.