Senior AI Analyst

About Dun & Bradstreet

Dun & Bradstreet is a globally recognized leader in providing business decisioning data and analytics. Their Data Cloud powers innovative solutions, enabling businesses worldwide to enhance revenue, minimize costs, reduce risks, and drive transformation. Established in 1841, Dun & Bradstreet serves companies of all sizes to effectively manage risk and identify opportunities.

Role Overview and Responsibilities

• Collaborate closely with both domestic and international data science teams and analytics leaders to achieve project goals promptly.
• Support and actively contribute to Generative AI projects across the organization.
• Create and deploy Generative AI applications utilizing large language models (LLMs) and AI toolkits.
• Utilize advanced AI models like GPT, Gemini, and LLaMA to develop AI-powered solutions.
• Design and manage complex multi-agent AI frameworks.
• Build solutions leveraging technologies such as LangChain, LangGraph, FastAPI, Streamlit, Chainlit, and Transformer-based libraries.
• Integrate AI functionalities into enterprise-grade applications via APIs and cloud-native services.
• Architect and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Qdrant, Weaviate, and ChromaDB.
• Engage with business stakeholders to gather requirements and translate them into AI-enabled solutions.
• Deliver analytical insights and strategic recommendations to address business challenges.
• Keep abreast of the latest advancements in Generative AI technologies, tools, and best practices.

Qualifications and Skills

• Possess a Bachelor's degree in Computer Science, Engineering, Statistics, or a related technical discipline.
• Have 6 to 8 years of professional experience in Data Analytics, Data Science, or specialized Generative AI roles.
• Demonstrated ability to develop and deliver data-driven and AI solutions in real-world production settings.
• Strong coding skills in Python, PySpark, and SQL.
• Hands-on experience with key data science libraries including Pandas, NumPy, Scikit-learn, and Matplotlib.
• Proficiency with GenAI platforms and tools such as OpenAI, Anthropic, Google Vertex AI, FastAPI, Gradio, and Streamlit.
• In-depth understanding of Retrieval-Augmented Generation (RAG) and Agentic AI architectures, especially using LangChain and LangGraph.
• Experience in API development and deployment using frameworks like FastAPI.
• Familiarity with MLOps methodologies and machine learning deployment processes is advantageous.
• Excellent analytical thinking, problem-solving capabilities, communication skills, and ability to manage stakeholder expectations.

Eligibility

Candidates who have completed any graduate degree are eligible to apply.