Research Scientist, Human Data & Evaluation

Contra; New York (NY); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Equity

Job summary

Contra Labs in New York City is seeking a Researcher to own end-to-end research engagements for client projects, from scoping to final delivery. You will translate ambiguous questions into rigorous studies, design methodologies, and produce actionable insights for frontier AI systems.

Join a human-centered AI lab that values originality, reports, and publications, while balancing rigor with startup speed in a multimodal design research environment.

Qualifications

• 3–5+ years of research experience in HCI, Human-Centered AI, ML or related field.
• Strong qualitative and quantitative research skills.
• Ability to design and execute rigorous research independently.
• Experience turning data into actionable insights.
• Proficiency in Python and/or SQL for analysis.
• Excellent written communication.

Responsibilities

• Translate ambiguous client questions into clear research questions and study designs.
• Develop research proposals and methodologies from scratch.
• Identify promising new research directions and push ideas.
• Design data collection strategies and evaluation protocols.
• Analyze qualitative and quantitative evaluation data.
• Produce client-ready reports and publications.
• Own research engagements from scoping to delivery.
• Collaborate with project leads and data/engineering teams.

Skills

Qualitative research

Quantitative research

Data analysis

Python

SQL

Written communication

Education

Master’s or PhD in HCI/ML/design research

Tools

Python

SQL

Job description

Contra Labs in New York City is seeking a Researcher to own end-to-end research engagements for client projects, from scoping to final delivery. You will translate ambiguous questions into rigorous studies, design methodologies, and produce actionable insights for frontier AI systems.

Join a human-centered AI lab that values originality, reports, and publications, while balancing rigor with startup speed in a multimodal design research environment.