Debt Capital Markets - AI trainer

Job Title: Debt Capital Markets - AI trainer

Job Type: Part-time

Location: Remote

Job Summary:

Join our customer's team as a Debt Capital Markets - AI trainer and contribute your finance expertise to the next generation of AI technology. In this unique role, you will leverage your understanding of debt capital markets and finance data to guide AI systems in navigating real-world spreadsheet complexity, ambiguous modeling, and unstructured financial information.

Key Responsibilities:

• Annotate, seed, and review diverse finance datasets, including messy, incomplete, or ambiguous inputs.
• Collaborate closely with AI development teams to train models for interpreting and normalizing “in-the-wild” finance artifacts.
• Develop and refine data seeding strategies to enable robust AI learning from real-world spreadsheet scenarios.
• Support iterative training flows where structure may evolve after initial data seeding.
• Provide high-quality, precise written and verbal feedback on model outputs and training data.
• Help reconstruct, extend, or standardize financial models as part of the seeding and training process.
• Contribute finance-adjacent knowledge to enhance the relevance of AI solutions in actual market contexts.

Required Skills and Qualifications:

• Deep familiarity with debt capital markets, finance data, and market instruments.
• Exceptional written and verbal communication skills with the ability to articulate complex finance concepts clearly.
• Experience working with spreadsheets, databases, or unstructured finance datasets.
• Strong analytical problem-solving capabilities and attention to detail.
• Comfortable operating with ambiguity, evolving data structures, and partial information.
• Ability to collaborate remotely and asynchronously across US, Canada, UK, and EU teams.
• Self-motivated and able to work independently on broad, messy data seeding projects.

Preferred Qualifications:

• Practical experience with AI or machine learning projects in finance or adjacent sectors.
• Background working on finance-adjacent annotation, data labeling, or model training initiatives.
• Understanding of data normalization, model input structuring, or prompt engineering concepts.