LLM & Agentic AI Data Scientist (R-18519) en Dnb

As part of Dun & Bradstreet's Data & Analytics team, you will participate in all aspects of modeling engagement, including design, development, validation, calibration, documentation, approval, implementation, monitoring, and reporting. You will research complex business issues and recommend solutions, including model features, end products and any data required to support growing Dun & Bradstreet initiatives. This role drives innovation, helps design and deploy agentic systems that orchestrate LLMs and other AI components to deliver structured, transparent, and scalable insights.

Responsibilities:

Perform machine learning and LLM-driven developments on very large and heterogeneous datasets from Dun & Bradstreet’s unique sources as well as unstructured data such as textual documents, online content, and multimodal sources.

Design, implement, and optimize agentic pipelines that combine LLMs, retrieval systems, and decision-making agents to power data quality processes, new products, and customer-facing solutions.

Develop models, embeddings, and new variables through state-of-the-art machine learning, NLP, and generative AI techniques

Translate results into actionable insights through reports, visualizations, or interactive prototypes.

Establish and maintain strong relationships with key business stakeholders.

Contribute expertise on new data sources and advanced AI architectures, collaborating closely with product, engineering, and business teams

Stay at the forefront of research and applied innovation in LLMs, agents, and scalable AI Orchestration, focusing on addressing specific customer needs and use cases.

Serve as a Subject Matter Expert on predictive models within the team and with business users; consult with the business, as appropriate, on predictive modeling solutions.

Requirements:

Master’s Degree in a quantitative/applied field preferred (Statistics, Econometrics, Computer Science, Operations Research, Mathematics, Engineering).

8+ years’ operating successfully in data science roles, especially roles requiring cross-company collaboration and disciplined delivery of initiatives.

2–6 years of experience in analytics, with at least 1 year focused on LLMs, agentic processes, or generative AI

Hands-on experience with deep learning/transformer models, LLM fine-tuning, prompt engineering agent development/orchestration, and data visualization is a strong advantage.

Ability to program in other statistical analysis languages, proficiency in programming languages (Python, R, SQL).

Experience in feature engineering, automation, network analysis or Natural Language Processing.

Ability to manage multiple assignments, many of which have challenging timelines.

Ability to work independently, as well as collaborate effectively in a team environment.

Excellent communication and presentation skills.

Proficiency in Microsoft Office Suite.

Show an ownership mindset in everything you do. Be a problem solver, be curious and be inspired to take action. Be proactive, seek ways to collaborate and connect with people and teams in support of driving success.

Continuous growth mindset, keep learning through social experiences and relationships with stakeholders, experts, colleagues and mentors as well as widen and broaden your competencies through structural courses and programs.

Where applicable, fluency in English and languages relevant to the working market.

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