Research Scientist (Measurement and Evaluation)
Designs and leads empirical studies evaluating ambient AI impact on patient outcomes and care quality using large-scale clinical data. Develops measurement frameworks and collaborates with product teams; requires PhD and strong publication record in evaluation research.
About the job
What You’ll Do
- Design and conduct evaluations of Abridge models and products
- Engage with external researchers and other stakeholders on designing and conducting research on ambient AI and research that leverages Abridge data
- Develop a strong user-centric and patient-centric mindset, grounding the research in empathy for the real world experience of providers and patients
- Collaborate across our cross-functional product teams to ensure the research is deeply informed by current practices and our product roadmap
- Write technical reports and give presentations to internal and external stakeholders
- Actively contribute to the wider research community by publishing original research in leading peer-reviewed publication venues
- Mentor research interns
What You’ll Bring
- PhD in statistics, biostatistics, computer science, economics, information systems, clinical informatics, or a related field.
- Expertise in rigorous quantitative or mixed-methods approaches for conducting evaluations using observational and experimental data.
- Strong research track record in evaluation and measurement, as evidenced by high-impact publications at peer-reviewed journals or conferences.
- A problem-before-method mindset. You do not change the question to make it amenable to simple analysis, but instead push the methodological frontier to solve the real world problems that matter to health systems, providers, and patients.
- A curious, adaptable, and proactive mindset, with a desire to learn and grow as a researcher in a fast-paced startup environment.
- Passion for and understanding of Abridge’s mission.
Compensation and Equity
Competitive compensation and equity grants for full time employees.
Skills
Statistics, Biostatistics, Computer Science, Quantitative Methods, Mixed-Methods, Observational Data, Experimental Data, Clinical Informatics, Evaluation Frameworks, Measurement Science
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