Key Responsibilities and Required Skills for a Voice Research Coordinator

🎯 Role Definition

The Voice Research Coordinator is the organizational cornerstone of our advanced research and development initiatives in speech and voice technology. This individual acts as the central hub, connecting researchers, engineers, data annotators, and study participants to ensure the seamless execution of data collection and research projects. More than just a project manager, the coordinator is a hands-on problem-solver responsible for the integrity of our data pipelines, the well-being of our research participants, and the timely delivery of high-quality datasets that fuel our machine learning models. This role is perfect for a highly organized and detail-oriented professional who is passionate about the intersection of language, technology, and human interaction.

📈 Career Progression

Typical Career Path

Entry Point From:

Research Assistant or Lab Manager (in academia or industry)
Technical Project Coordinator
Data Analyst or Data Annotator with leadership experience

Advancement To:

Senior Voice Research Coordinator or Research Program Manager
Research Operations Manager
Data Scientist, specializing in speech and audio

Lateral Moves:

UX Researcher (specializing in voice interfaces)
Product Analyst (for voice-enabled products)

Core Responsibilities

Primary Functions

Design, plan, and execute end-to-end voice data collection studies, from defining initial requirements with research scientists to final data delivery.
Develop comprehensive project plans, including detailed timelines, resource allocation, and budget tracking for multiple concurrent research initiatives.
Recruit, screen, and onboard a diverse pool of human participants for research studies, ensuring demographic targets and linguistic profiles are met.
Act as the primary point of contact for research participants, managing all communications, scheduling, and ensuring a positive and professional experience.
Create and maintain essential study documentation, including consent forms, study protocols, and instructional materials, ensuring compliance with legal and ethical standards.
Manage the logistics of data collection sessions, whether remote or in-person, including the setup and troubleshooting of specialized audio recording equipment and software.
Oversee the entire data lifecycle, from initial collection and secure storage to annotation, quality control, and final hand-off to engineering teams.
Implement and manage rigorous quality assurance (QA) protocols for collected audio data, meticulously checking for environmental noise, data corruption, and adherence to recording standards.
Coordinate with external vendors and partners for specialized data collection or annotation services, managing contracts and ensuring quality of deliverables.
Develop detailed scripts, prompts, and scenarios for voice collection based on specific phonetic, linguistic, and acoustic requirements provided by the research team.
Manage participant compensation and reimbursement processes, ensuring accuracy and timeliness.
Proactively identify and resolve logistical, technical, and interpersonal issues that may arise during the course of a research study.
Maintain and organize large, complex datasets of speech and audio, ensuring data is properly labeled, structured, and accessible for researchers.
Collaborate closely with cross-functional teams, including linguists, data scientists, software engineers, and product managers, to align on project goals and requirements.
Prepare and deliver regular, detailed status reports and presentations on project progress, risks, and outcomes to key stakeholders and leadership.
Refine and continuously improve data collection methodologies and operational workflows to increase efficiency, scale, and data quality.
Ensure all research activities strictly adhere to data privacy and security policies, such as GDPR and CCPA, particularly when handling sensitive participant information.
Train and supervise data annotators, providing clear guidelines and feedback to ensure high levels of accuracy and consistency in data labeling.
Curate and pre-process raw data for machine learning applications, which may involve tasks like audio segmentation, transcription alignment, and basic data cleaning.
Conduct initial exploratory analysis of collected data to identify trends, anomalies, or quality issues that require further investigation.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis from the research and product teams.
Contribute to the organization's broader data strategy and roadmap by providing insights from on-the-ground research operations.
Collaborate with business units to translate data needs into concrete engineering and research requirements.
Participate in sprint planning and agile ceremonies within the data and research teams, representing the operational aspects of ongoing projects.

Required Skills & Competencies

Hard Skills (Technical)

Scripting Proficiency: Practical ability to write and modify simple scripts, typically in Python or Bash, for data manipulation, automation, and file management.
Acoustic Analysis Software: Hands-on experience with tools like Praat or Audacity for inspecting audio waveforms, spectrograms, and performing basic acoustic measurements.
Data Querying: Foundational knowledge of SQL for querying relational databases to track participant data, project status, and metadata.
Project Management Software: High proficiency in using tools like Jira, Confluence, Asana, or similar platforms to manage complex project timelines, tasks, and documentation.
Spreadsheet Mastery: Advanced skills in Microsoft Excel or Google Sheets for data tracking, reporting, budget management, and creating participant schedules.
Command-Line Interface (CLI): Comfort working in a terminal environment for file system navigation, running scripts, and using tools like SSH or Git.
Data Annotation Tools: Familiarity with various data labeling platforms and an understanding of the workflows involved in transcription, segmentation, and event labeling.
Audio Hardware & Software: Experience setting up, configuring, and troubleshooting microphones, audio interfaces, and recording software.
Version Control Systems: Basic understanding of Git for managing code, scripts, and documentation in a collaborative environment.
Data Privacy Regulations: Working knowledge of the principles of data privacy and security standards such as GDPR, HIPAA, or CCPA.

Soft Skills

Meticulous Attention to Detail: An exceptional ability to spot inconsistencies and errors, whether in a dataset, a project schedule, or a consent form.
Exceptional Organization & Time Management: The capacity to juggle multiple projects, competing deadlines, and a wide array of tasks without sacrificing quality.
Proactive Problem-Solving: An instinct for anticipating potential issues before they become major problems and independently developing creative solutions.
Clear & Empathetic Communication: The skill to communicate complex technical and logistical information clearly to diverse audiences, from engineers to non-technical research participants.
Stakeholder Management: The ability to build strong, collaborative relationships with internal teams, external vendors, and research subjects.
Adaptability & Composure: The resilience to thrive in a fast-paced, dynamic research environment where priorities and protocols can change quickly.

Education & Experience

Educational Background

Minimum Education:

A Bachelor's degree is required.

Preferred Education:

A Master's degree in a relevant field is highly preferred and provides a significant advantage.

Relevant Fields of Study:

Linguistics / Phonetics
Computer Science
Cognitive Science / Psychology
Communication Sciences and Disorders
Data Science

Experience Requirements

Typical Experience Range: 2-5 years of direct experience in a research coordination, project management, or data management role.

Preferred:
This role requires someone with demonstrated experience in an academic research lab or a corporate R&D environment, specifically involving human subjects research. Ideal candidates will have a proven track record of managing data collection projects from start to finish. Experience working directly with speech data, linguistics, or AI/ML teams is a strong plus. You should be comfortable being the person who ensures the 'i's are dotted and the 't's are crossed, enabling our research to move forward efficiently and ethically.