Data Scientist driving LLM-driven innovations within Dun & Bradstreet’s Data & Analytics team. Aiming for scalable AI insights through complex modeling engagement and business research.
Responsibilities
Participate in all aspects of modeling engagement, including design, development, validation, calibration, documentation, approval, implementation, monitoring, and reporting.
Research complex business issues and recommend solutions, including model features, end products and any data required to support growing initiatives.
Drive innovation by helping design and deploy agentic systems that orchestrate LLMs and other AI components to deliver structured, transparent, and scalable insights.
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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