The work
You will evaluate multiple chatbot responses against defined business criteria and produce final assessments. You will also apply business analysis methods to requirements, workflows, processes, data, and functional needs.
The project supports AI applications in accounting and finance. Your work will include documenting requirements, identifying gaps, preparing recommendations, and helping teams turn business needs into clear specifications.
- Gather, analyze, and document business requirements.
- Compare chatbot outputs and rate them against defined criteria.
- Translate business needs into clear functional specifications.
- Review workflows, processes, and data for gaps and improvement opportunities.
- Create reports, dashboards, process maps, and related documentation.
- Support project planning, testing, implementation, and user acceptance testing.
- Coordinate with technical and cross-functional teams.
- Track risks, dependencies, progress, and key performance indicators.
- Present findings, insights, and recommendations to stakeholders.
What it pays and takes
The role details do not provide a pay rate. This is a freelance, part-time project open to candidates in the United States.
- Schedule: 20 or more hours per week.
- Project length: 16 weeks.
- Weekly schedule: five days, including weekends when required, with two weekdays off.
- Location: United States.
- Language: Fluent in English.
- Experience: At least two years as a Business Analyst or in a similar role.
- Required skills: Requirements gathering, process mapping, analysis, problem-solving, communication, and documentation.
- You should be able to work with cross-functional teams, manage multiple priorities, and use careful judgment when evaluating chatbot outputs against business requirements.
- Knowledge of Agile or Scrum is helpful.
- Familiarity with accounting and finance applications is helpful.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training is the human work behind systems that learn from examples. Contributors review and rate model responses so AI systems can produce more useful results, and experienced professionals are needed when evaluations depend on business judgment and specialized methods.