Human Systems Research Scientist (M.S. + 4 years of experience or Ph.D.)

About Exponent

Exponent is the only premium engineering and scientific consulting firm with the depth and breadth of expertise to solve our clients’ most profoundly unique, unprecedented, and urgent challenges.  

 

Our vision is to engage multidisciplinary teams of science, engineering, and regulatory experts to empower clients with solutions that create a safer, healthier, more sustainable world. For over five decades, we've connected the lessons of past failures with tomorrow's solutions to advise clients as they innovate technologically complex products and processes, ensure the safety and health of their users, and address the challenges of sustainability.  

 

Join our team of experts with degrees from top programs at over 500 universities and extensive experience spanning a variety of industries. At Exponent, you’ll contribute to the diverse pool of ideas, talents, backgrounds, and experiences that drives our collaborative teamwork and breakthrough insights. Plus, we help you grow your career through mentoring, sponsorship, and a culture of learning. Thanks for your interest in joining our team!   

 

Key statistics: 

• 950+ Consultants 
• 640+ Ph.D.s 
• 90+ Disciplines 
• 30+ Offices globally 

Our Opportunity

We are currently seeking a Human Systems Research Scientist for our Data Sciences Practice in New York, NY. This role is for an accomplished human-subjects researcher who can lead complex data-collection programs, work hands-on with technical systems, and guide interdisciplinary teams in fast-moving, ambiguous project environments.

You will be responsible for

• 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

You will have the following skills and qualifications

• 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. Please note that recent M.S. graduates do not meet this requirement.
• 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
• Preferred: First-author research publication experience and/or presentation experience in academic, technical, industry, or client-facing forums

Technical and data skills

• 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

Working style and travel

• 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, coor