Data AI Trainer

- Design and deliver training that enables teams to evaluate process changes - such as adopting new data pipelines, switching validation methods, or implementing AI-assisted workflows - and quantify their impact on dataset quality and business outcomes. - Create hands-on labs where teams design experiments on real Bloomberg datasets—testing pipeline changes, evaluating new tools, and measuring quality improvements. - Explain core statistical concepts (sampling, correlation, causation, p-values) and AI/ML fundamentals (prompt engineering, model validation) in the context of data quality and process optimization. - Incorporate AI-assisted tools (e.g., GitHub Copilot, ChatGPT, NotebookLM) into training design and delivery. - Ensure teams maintain the highest standards for data quality, observability, and governance, alongside the implementation of transformative AI technologies. - Partner with engineers and domain experts to ensure we’re meeting client needs and leveraging the best technology solutions. - Develop self-service materials that enable teams to independently design experiments and adopt new tools. - Stay current with emerging experimentation methods, AI tools, and financial market dynamics—continuously refining curricula to meet evolving Data organization needs and business priorities. - Commitment to cultivating a continuous learning culture across technical teams.