Research Scientist, Evaluations - Simulation Benchmarks

Tenera, Inc; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Tenera, Inc. seeks a Research Scientist in Evaluations to lead an ambitious, open-ended research program with substantial uncertainty. You will define questions, develop original approaches, and test ideas with scientific rigor, enjoying autonomy for independent work.

You will design experiments, build evaluation benchmarks, and manage data-labeling pipelines, documenting uncertainty and ground truth while driving results to publishable conclusions.

Qualifications

• Track record of original empirical or methodological research in statistics, machine learning, behavioral science, psychometrics, computational social science, or a related field.
• Exceptional creativity and execution in building experiments and solving problems.
• Comfort with substantial uncertainty and sustained independent work; ability to publish results and revise conclusions.

Responsibilities

• Define and pursue an ambitious research agenda in simulation evaluation.
• Turn loosely defined questions into concrete research plans with milestones.
• Design and implement evaluation methods grounded in observed behavior and data.
• Build and operate data-labeling pipelines with guidelines and quality checks.
• Design controlled comparisons, holdout datasets, and replication studies.
• Study validity, reliability, and generalization across populations and contexts.
• Own execution from hypothesis through reproducible results and scientific writing.

Skills

Python

Data analysis

Statistical inference

Experimental design

Scientific writing

Independent research

Tools

Annotation tools

Job description

Tenera, Inc. seeks a Research Scientist in Evaluations to lead an ambitious, open-ended research program with substantial uncertainty. You will define questions, develop original approaches, and test ideas with scientific rigor, enjoying autonomy for independent work.

You will design experiments, build evaluation benchmarks, and manage data-labeling pipelines, documenting uncertainty and ground truth while driving results to publishable conclusions.