Data Scientist (R-19646)

**Shape the Future with Dun & Bradstreet** At Dun & Bradstreet, we believe data has the power to create a better tomorrow. As a global leader in business decisioning data and analytics, we help companies worldwide grow, manage risk, and innovate. Since 1841, businesses have trusted us to turn uncertainty into opportunity. We’re a diverse, global team that values creativity, collaboration, and bold ideas. Are you ready to make an impact and help shape what’s next? Join us! Explore opportunities at [](http://dnb.com/careers). **Why We Work at Dun & Bradstreet** 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 ### Key Responsibilities: 1. **Agent Development & Architecture** • Build agentic workflows using LangChain/LangGraph and similar frameworks. • Develop autonomous agents for data validation, reporting, document processing, and domain workflows. • Deploy scalable, resilient agent pipelines with monitoring and evaluation. 2. **GenAI Application Engineering** • Develop GenAI applications using models like GPT, Gemini, and LLaMA. • Implement RAG, vector search, prompt orchestration, and model evaluation. • Partner with data scientists to productionize POCs. 3. **Data & Platform Engineering** • Build distributed data pipelines (Python, PySpark). • Develop APIs, SDKs, and integration layers for AI-powered applications. • Optimize systems for performance and scalability across cloud/hybrid environments. 4. **MLOps / LLMOps** • Contribute to CI/CD workflows for AI models—deployment, testing, monitoring. • Implement governance, guardrails, and reusable GenAI frameworks. 5. **Collaboration & Stakeholder Engagement** • Work with analytics, product, and engineering teams to define and deliver AI solutions. • Participate in architecture reviews and iterative development cycles. • Support knowledge sharing and internal GenAI capability building. ### Key Skills & Requirements: • 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