Data Scientist (R-19646)

We are at a transformational moment in our company journey - and we’re so excited about it. Each day, we are finding new ways to strengthen our award-winning culture, and to accelerate creativity, innovation and growth. Our purpose is to help customers improve business performance with Dun & Bradstreet’s Data Cloud and Live Business Identity, and we’re wildly passionate and committed to this purpose. So, if you’re looking to make an immediate impact at a company that welcomes bold and diverse thinking, come join us!

The Role: We are looking for an experienced AI Engineer to design, build, and operationalize AI driven solutions for our global Analytics organization.
The ideal candidate will have strong hands on expertise in Python, PySpark, agentic workflow development, and modern GenAI frameworks, with experience building scalable applications using LLMs, retrieval systems, and automation pipelines.
You will work closely with data scientists, MLOps engineers, and business stakeholders to build intelligent, production grade systems that power

• 5-8 years of experience in AI/ML engineering, data science, or software engineering, with at least 4 years focused on GenAI.

• Strong programming expertise in Python, distributed computing using PySpark, and API development.
• Hands on experience with LLM frameworks (LangChain, LangGraph, Transformers, OpenAI/Vertex/Bedrock SDKs).
• Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration.
• Solid understanding of GenAI concepts: prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering.
• Experience with cloud platforms (Azure/AWS/GCP), containerization (Docker), and CI/CD pipelines for ML/AI.
• Strong problem solving, system design thinking, and ability to translate business needs into scalable AI solutions.
• Excellent verbal, written communication and presentation skills.

Good to Have
• Experience in workflow automation and building reusable AI components.
• Background in analytics, statistical models, or enterprise data products.
• Experience with MLOps / LLMOps tooling