Data AI Trainer

Data AI Trainer

Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock – from around the world. In the Data department, we are responsible for delivering this data, news, and analytics through innovative technology — quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies and implement technology solutions to enhance our systems, products, and processes — all while providing platinum customer support to our clients.

The Team

At Bloomberg, our team is responsible for onboarding our junior data engineers, as well as providing learning opportunities to develop the skills of our nearly 2,000 Data employees. We collaborate with all teams across Data to ensure that we deliver the highest quality educational development. We also roll up our sleeves to create our own training and applied exercises. You can support our purpose by preparing Data teams with the confidence to apply AI tools into data processing workflows responsibly and effectively. The work we accomplish will contribute to AI products used by external Bloomberg clients.

We strive to make our curriculum exciting for both trainers and trainees; we use interactive technology, peer learning, and a highly collaborative team culture to ensure success for everyone. We encourage participation and provide opportunities for trainees to learn from each other and the professionals within Data.

Responsibilities

• 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.

Qualifications

• 3+ years applied experience in applied AI solutions.
• Bachelor’s degree or higher in Computer Science, Engineering, Data Science, or other data-related field.
• Experience mentoring or teaching technical material, with a passion for continuous learning and knowledge sharing.
• Ability to translate technical concepts into clear learning content and documentation.
• Strong communication and teaching abilities— a proven track record explaining complex quantitative concepts to both technical and non-technical audiences through clear examples and hands-on exercises.
• Ability to identify learning needs through stakeholder consultation and translate them into scalable, practical training solutions.
• Understanding of data quality metrics (accuracy, completeness, timeliness) and concepts (data observability, governance) and how to assess them through statistical methods.
• Proven problem-solving skills and adaptability in evolving, fast-paced environments.
• Collaborative approach to partnering across global teams and aligning with business priorities.
• Effective project management skills to develop and manage a roadmap and deliver milestones in a timely manner.
• Ability to flexibly adapt to a changing environment.
• Interest in financial market datasets and their application to data and AI solutions.

Preferred Qualifications

• Familiarity with modern data tools and frameworks (e.g., Airflow, Dagster, dbt, Spark, cloud data platforms).
• Certification in DAMA CDMP, EDM DCAM or similar.
• Examples of technical content you've created—whether documentation, tutorials, presentations, or internal training materials.
• Hands-on experience with financial data, market data, or other business-critical datasets.

Key skills/competency

• AI Solutions Development
• Data Quality & Governance
• Technical Training Design
• Statistical Analysis
• Machine Learning Fundamentals
• Prompt Engineering
• Curriculum Development
• Stakeholder Collaboration
• Project Management
• Financial Market Data