Responsibilities
- Lead local and global human-subjects data-collection programs, including study design, protocols, participant workflows, quality control, and delivery
- Coordinate interdisciplinary teams across Data Sciences, Human Factors, Biomechanics, Health Sciences, and engineering
- Troubleshoot prototype hardware, sensor arrays, mobile devices, operating systems, and research software in real-world study environments
- Build lightweight tools for tracking, automation, visualization, data processing, and quality control
- Document data flows from collection through delivery, including transformations, validation checks, exceptions, and quality gates
- Develop and maintain strong client and stakeholder relationships
Requirements
- M.S. with at least 4 years of post-degree experience or a Ph.D. in Human-Computer Interaction, Human Factors, Biomechanics, Ergonomics, Neuroscience, Cognitive Science, Psychology, Kinesiology, or a relevant engineering/scientific field
- Presently legally authorized to work in the United States; no immigration sponsorship or processing required
- Record of leading complex human-subjects research, user studies, or large experimental data-collection efforts
- Experience in one or more areas such as HCI, human factors, biomechanics, ergonomics, behavioral or systems neuroscience, cognitive science, sensing systems, wearable devices, motion capture, or mobile-device research
- Ability to turn ambiguous scientific or operational questions into rigorous approaches and communicate insights clearly to technical, executive, and client audiences
Nice to Have
- First-author research publication experience and/or presentation experience in academic, technical, industry, or client-facing forums
- Hands-on comfort with instrumentation, data acquisition, synchronization, hardware/software troubleshooting, and operating-system quirks
- Practical scripting or programming skills, such as Python, JavaScript, MATLAB, R, or similar tools for automation, data checks, and workflow support
- Experience constructing data pipelines or processing workflows that transform raw human-subjects, sensor, device, or interaction data into analysis-ready datasets
- Experience developing custom data-quality methods, audits, dashboards, or automated checks for missingness, synchronization issues, labeling errors, outliers, protocol deviations, or other sources of noise
- Familiarity with machine learning concepts, dataset evaluation, labeling workflows, or model-assisted approaches to improving data quality
Work Arrangement
On-site — New York, NY
Additional Information
- Exceptional organization, attention to detail, practical judgment, and willingness to do hands-on work that may fall outside traditional data science or academic research roles
- Strong interpersonal judgment and people-management skills, including the ability to motivate teams, coordinate contributors, and maintain high standards under time pressure
- Excellent verbal and written communication skills; ability to work independently and in multidisciplinary teams
- Flexibility to support changing study timelines, participant schedules, evening/weekend needs, and domestic or international travel, including extended engagements of 2–4 weeks
- Applicants are encouraged to submit a CV (Curriculum Vitae) with publications (feel free to include publications that are in review or pending)
