Data Scientist-Advanced Analytics

A career at IBM Consulting offers the opportunity to work closely with leading companies across various industries, helping them navigate their hybrid cloud and AI transformations. Leveraging IBM’s advanced technologies, strategic partnerships, and Red Hat’s capabilities, you will play a pivotal role in driving meaningful change and accelerating client outcomes. 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 supported.

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 that integrate AI agents with enterprise applications, APIs, and various data sources.
- Develop Retrieval-Augmented Generation (RAG) solutions utilizing vector databases, embeddings, and enterprise knowledge repositories.
- Fine-tune, evaluate, and optimize foundation models and large language model (LLM) based applications to enhance performance.
- Utilize programming languages, particularly Python, within development environments such as PyCharm, VS Code, and Jupyter Notebooks.
- Apply knowledge of the SAP ecosystem, especially in Enterprise Asset Management (EAM), to support AI-driven solutions.

Required Qualifications

- Strong expertise in generative and agentic AI, including familiarity with large language models (LLMs) like GPT, Claude, Llama, and Mistral.
- Experience with prompt engineering and RAG methodologies.
- Proficiency in agentic AI frameworks such as LangGraph, CrewAI, AutoGen, and Semantic Kernel.
- Ability to design and orchestrate multi-agent workflows and apply Model Context Protocol (MCP) and agent integration patterns.
- Knowledge of AI evaluation techniques, observability, and governance practices.
- Solid foundation in traditional AI and data science, including machine learning, deep learning, statistical modeling, and predictive analytics.
- Experience with natural language processing (NLP), classification, clustering, and time series forecasting.
- Strong skills 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 the SAP ecosystem, particularly in Enterprise Asset Management, is highly desirable.
- Experience with data and cloud technologies including SQL, NoSQL, vector databases, and data engineering concepts such as ETL/ELT.
- Exposure to cloud AI services from platforms like Azure AI, Databricks, AWS, or Google Cloud Platform (GCP).
- Knowledge of MLOps and LLMOps practices, including CI/CD pipelines and containerization technologies.

This role offers a dynamic environment where innovation meets real-world impact, supported by IBM’s global resources and a culture that values growth and collaboration.