We are seeking a detail-oriented and research-driven Associate to review and edit audio transcripts of recorded interviews with influential buyers of financial services (Buy side ). You will play a critical role in transforming raw textual data from survey responses into structured information. You will be gathering relevant color/ information from the transcripts with a focus on messages that are of interest to our sell side clients. This opportunity will provide you with solid insights into the financial world as you get to hear from the influential decision makers on the Buy side.
You will also act as a reviewer to verify GenAI outputs and correct incomplete, inaccurate, or mismatched responses. You will help ensure transcription accuracy, question mapping alignment, and grammatically clean responses. Giving feedback to the Gen AI team to refine future model output.
Key Responsibilities: - Up to date on financial industry developments to understand the context better while editing interview transcripts
- Parse relevant color/ information from the transcripts by analyzing open-ended survey questions for the service providers
- Apply grammar, clarity, and standard formatting as needed
- Review Gen AI-extracted transcripts and ensure correctness
- Compare mapped questions with transcript and verify alignment
- Provide feedback on transcription quality and suggest improvements
- Provide feedback to prompt engineering team on rework causes
- Publish reports on accuracy and prompts quality
Skills & Requirements: - Ability to understand English language, identify speakers, and discern nuances in the speech
- Financial services background is a must with exposure to Corporate and Investment Banking products
- Solid understanding of English grammar and punctuation rules is crucial for producing accurate client ready open-ended comments
- Ability to adapt to different audio styles, accents, and formatting requirements
- Ability to research and verify information to ensure accuracy in the transcription
- Attention to detail is critical for identifying and correcting errors in transcriptions
