Overview

You have hands-on experience in data science, machine learning, and applied AI in enterprise environments.

Responsibilities

• Translate business challenges into data-driven solutions — exploring data, engineering features, and developing models for classification, forecasting, anomaly detection, and recommendation scenarios.
• Design, train, evaluate, and optimize ML models and statistical solutions for enterprise use cases of moderate complexity.
• Develop generative AI components using LLMs, RAG pipelines, embeddings, vector databases, and agentic AI frameworks.
• Define evaluation metrics, validate model performance, monitor deployed solutions, and implement improvements to accuracy and reliability.
• Collaborate with AI Engineers, Data Engineers, Product Managers, and UX teams to deliver components and communicate technical findings to customer stakeholders.

Qualifications

• Solid proficiency in Python and SQL, with hands-on data science, machine learning, or applied AI experience.
• Functional experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or XGBoost, with a working knowledge of statistics, feature engineering, and model evaluation.
• Practical exposure to large language models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG) pipelines, embeddings, vector databases, or AI orchestration frameworks such as LangChain or LangGraph.
• Experience deploying ML or AI solutions on cloud platforms using machine learning operations (MLOps) practices.
• Knowledge with SAP data landscapes — such as SAP S/4HANA, SAP Business Technology Platform (BTP), or SAP AI Core — is an advantage.