Dun & Bradstreet

Events

• Support the development and execution of D&B’s enterprise-wide event strategy to advance business objectives, elevate brand perception, and deepen client engagement.
• Contribute to high impact experiential programs, including immersive brand activations, integrated event marketing, standout customer experiences, team member moments, and strategic sponsorships aligned to commercial priorities.
• Partner closely with the Director of Events & Experiences to help drive operational excellence, consistently raise the bar on experience design, and deliver high quality events across regions.
• Collaborate cross functionally with marketing peers, business leaders, and other stakeholders to translate business priorities into thoughtful, well executed experiential and sponsorship programs with measurable results.
• Help concept, refine, and execute innovative event programs that strengthen relationships with clients and partners and contribute to commercial growth initiatives.
• Manage end to end event operations, including budget tracking, vendor coordination, sourcing, logistics, risk mitigation, and compliance with internal standards and policies.
• Support the execution of both proprietary and third party events and sponsorships across D&B’s portfolio, including Agenda and content development, Production and vendor management, Contract and financial coordination, Communication with attending teams to ensure preparedness, and Post-event reporting and measurement.

Events & Experiences Manager

Analyst

• Work across range of Analytical Solutions that includes Implementation, Testing, Validation, Documentation, Monitoring, and Reporting.
• Extracting large data from various data sources using multiple tools such as Python/ Pyspark /Hadoop/ SQL/ etc., to perform multiple analysis.
• Architect and create Agentic solutions to achieve operational efficiency.
• Creating data pipelines, performing model scoring, and validating model results. Utilize custom built automated frameworks for all deliverables.
• Work on MLOps/LLMOps frameworks to deploy, monitor and validate AI/ML models.

Analyst II

• Work across range of Analytical Solutions that includes Implementation, Testing, Validation, Documentation, Monitoring, and Reporting.
• Extracting large data from various data sources using multiple tools such as Python/ Pyspark /Hadoop/ SQL/ etc., to perform multiple analysis.
• Creating data pipelines, performing model scoring, and validating model results. Utilize custom built automated frameworks for all deliverables.
• Work with GenAI Frameworks/Tools such as Microsoft CoPilot Studio, LangGraph, etc. to create agentic solutions.
• Work with multiple teams such as Data Supply, ML Engineers, Technology to implement ML/GenAI solutions and models.

Data Scientist

• 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
• 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
• 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
• Contribute to CI/CD workflows for AI models—deployment, testing, monitoring
• Implement governance, guardrails, and reusable GenAI frameworks
• 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

Financial Planning and Analysis

• Role focused on Workday Financials, particularly covering configuration, integration, and reporting.
• Foundational Data Model (FDM) Maintenance: Maintain and update Workday’s Foundational Data Model – the multi-dimensional chart of accounts (work tags) framework that underpins all financial transactions and reporting.
• Workday Financials Configuration: Configure and support the Procurement, Accounts Payable (AP), Accounts Receivables (AR), Banking, General Ledger (GL), Assets and Workday Projects modules in Workday Financial Management.
• Integration Development (Workday Studio): Design, develop, test, and deploy integrations between Workday and other internal or third-party systems using Workday Studio.
• Reporting and Analytics Support: Develop and maintain custom reports and dashboards for all Workday financial modules using Workday’s reporting tools.
• Compliance and Best Practices: Ensure Workday configurations and integrations adhere to internal controls, data security policies, and compliance requirements.
• Continuous Improvement: Stay up-to-date with Workday’s semi-annual release updates, especially new features related to Financial Management and Integration.
• Documentation and Training: Create and maintain detailed documentation for configurations, integration designs, workflows, and any custom solutions implemented.

Senior Workday Finance Specialist

• Build agentic workflows using Lang Chain/Lang Graph and similar frameworks
• Develop autonomous agents for data validation, reporting, document processing, and domain workflows
• Deploy scalable, resilient agent pipelines with monitoring and evaluation
• 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
• 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
• Contribute to CI/CD workflows for AI models—deployment, testing, monitoring
• Implement governance, guardrails, and reusable GenAI frameworks
• 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

Senior Analyst

• Develop AI Agents: Design, code, and implement AI agents and copilots using Google's Gemini API and Microsoft Copilot Studio.
• Integrate Systems: Write robust Python code to connect AI agents with enterprise data sources, internal tools, and third-party services via REST APIs.
• Implement RAG Patterns: Build and refine Retrieval-Augmented Generation (RAG) pipelines to ensure agents provide accurate, context-aware responses grounded in proprietary data.
• Prompt Engineering: Craft, test, and iterate on effective prompts to guide agent behavior, ensuring reliability, safety, and desired outcomes.
• Full Lifecycle Development: Participate in the entire development lifecycle, from initial concept and design through to testing, deployment, and maintenance.
• Collaboration: Work closely with senior engineers to overcome technical challenges and with product managers to translate business requirements into functional agent capabilities.
• Troubleshooting: Debug and resolve issues in agent performance, whether they stem from the underlying LLM, the data pipeline, or the integration code.
• 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.

Data Science Analyst II

AI Engineer

• 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.
• 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.
• 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.
• Contribute to CI/CD workflows for AI models—deployment, testing, monitoring.
• Implement governance, guardrails, and reusable GenAI frameworks.
• 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.