The Research Scientist works directly with and reports to the Head Scientist. Together you will choose what to study, design the studies, define how results are measured, interpret the findings, and write them up for internal decision-making and, where appropriate, for academic publication. You will bring current methodological training in computational social science, data science, survey-methodology, human-computation interaction, and experiment design.
Responsibilities - Co-design and run studies that compare system-generated responses with responses from humans, using both qualitative and quantitative measures.
- Design, justify, and hone measures of performance.
- Apply and adapt state-of-the-art statistical techniques common to survey methodology and large-scale social data analysis, to benchmark and improve sample- and population-level simulation performance.
- Design evaluations and experiments with pre-specified analysis plans.
- Design and oversee human rating studies, including rater training, coding rubrics, and inter-rater agreement measurement.
- Design data collection from third-party data sources and directly from human participants, including longitudinal and repeated-measures studies.
- Contribute to the research agenda for new modeling approaches, including evaluation of fine-tuned models and of model calibration.
- Specify analyses precisely enough for an engineer to implement.
- Co-author papers, preprints, and presentations.
Qualifications- A completed or nearly completed PhD in computational social science, communication, HCI, sociology, political science, psychology, statistics, or a related field.
- Peer-reviewed publications on which you were a primary author.
- Working command of graduate-level statistical methodology common to social science, including the assumptions behind each.
- Experience analyzing large-scale telemetry and behavioral datasets, with attention to the validity of the resulting inference.
- Familiarity with machine learning, spanning classical, nonparametric, and LLM-based approaches, including how transformer-based language models work, what fine-tuning does, and how the major model families differ.
- Fundamentals of computing: file systems, software libraries, databases, SQL, and tabular data.
- Ability to work in an environment where most code is produced through agentic coding tools, and to read and check that code.
Preferred Qualifications- Experience fine-tuning open-weight language models on survey, experimental, or behavioral data.
- Experience building or evaluating calibrated confidence or error-prediction models, including deriving decision thresholds from human judgments.
- Experience measuring similarity of open-ended text, for example with embedding-based methods.
- Experience designing or running panel-based data collection with human participants.
- Experience presenting research at academic and/or industry conferences.
- Prior work on generative agents, digital twins, or simulation of human respondents.
- Familiarity with the startup ecosystem
- Familiarity with qualitative interviews and ethnographic methods
- Familiarity with topics in computational linguistics
