Financial Institutions - AI trainer

Job Title: Financial Institutions - AI trainer

Job Type: Part-time

Location: Remote

Job Summary:

Join our customer's team as a Financial Institutions - AI trainer and play a key role in shaping next-generation models tailored for real-world finance. You will engage with diverse, finance-adjacent data sources, helping AI understand, normalize, and extend messy financial artifacts and spreadsheets. This unique opportunity empowers you to influence the building blocks of powerful finance AI systems while collaborating remotely with contributors across the US, Canada, UK, and EU.

Key Responsibilities:

• Seed, organize, and label broad, unstructured data sets drawn from real-world finance artifacts.
• Collaborate with the team to design ambiguous, open-ended data flows for AI learning and problem solving.
• Guide AI models to reconstruct, normalize, or extend partially structured financial data.
• Contribute finance-adjacent expertise to projects involving messy spreadsheets and incomplete models.
• Craft concise, effective written and verbal feedback to improve AI model performance.
• Adapt quickly to evolving project needs, seeding data first and introducing structure as required.
• Maintain an agile approach in a fast-paced, experimental environment where flows and processes evolve.

Required Skills and Qualifications:

• Strong background or familiarity with financial institutions, banking, or finance adjacent domains.
• Exceptional written and verbal communication skills with attention to clarity and context.
• Experience working with unstructured, messy data in spreadsheets or similar formats.
• Ability to interpret and extend ambiguous, partially completed financial artifacts.
• Comfort working independently, remotely, and across geographically distributed teams.
• Demonstrated adaptability in dynamic, iterative project environments.
• Attention to detail and drive to improve AI performance through careful data curation.

Preferred Qualifications:

• Prior experience with data seeding for AI or machine learning projects.
• Exposure to finance data normalization, reconstruction, or similar data transformation tasks.
• Contributions to experimental or broad/messy data projects within the finance sector.