Senior Research Data Scientist

About Abridge

Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients. Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems. We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh.

The Role

Abridge is hiring a Senior Research Data Scientist to join our Strategic Research team. This role sits at the intersection of rigorous research, complex empirical data work, and deep cross-functional engagement with our commercial and builder organizations. You will be the team's resident expert on the data assets, structures, and pipelines that underpin our research — operating flexibly across the boundaries of data engineering, data analysis, and applied science to ensure the team can move quickly and confidently from raw data to credible insight. You will partner with research scientists to explore complex, messy real-world healthcare data: building and maintaining the data infrastructure the team depends on, developing and stress-testing metrics, conducting causal and descriptive analyses, and interrogating the assumptions embedded in our evaluation frameworks. You will work at startup pace with research rigor, with a mandate to go beyond off-the-shelf metrics and analyses to ensure that our evaluations capture what truly matters to clinicians and patients — not just what is easiest to measure. Equally important, you will translate complex analyses into clear, compelling narratives grounded in data, so that research insights inform product decisions, guide strategy, and help establish rigorous evidence for the real-world impact of ambient AI in healthcare.

About Strategic Research

The Strategic Research team at Abridge has two primary functions: (i) designing and conducting rigorous research studies investigating the impact of ambient AI as an intervention in partnership with collaborating health systems; and (ii) conducting health care research that leverages Abridge data, and supporting external research efforts to do the same. In addition to driving and supporting empirical studies of the impact of ambient AI-enabled technologies, the team works closely with our Science and Engineering teams on core model evaluation. The common thread to all our work is ensuring that every partner-facing research initiative meets the highest standards of rigour, credibility, and strategic value.

What You'll Do

Evaluation, Measurement, and Empirical Analysis

• Conduct quantitative evaluations of Abridge models and products using data from real-world deployments, offline evaluations, and customer feedback
• Develop and validate metrics that reflect meaningful outcomes for providers and patients — including assessing construct validity, characterizing measurement error, and surfacing selection bias in observational signals like user ratings and feedback
• Design and execute analyses that address the team's core research questions: validating automated evaluation frameworks against human judgment, characterizing heterogeneity in adoption and usage trajectories, estimating causal effects of ambient AI on clinical and operational outcomes, and extracting structured characterizations of clinical practice from unstructured conversation data
• Build deep familiarity with existing metrics and evaluation frameworks used at Abridge and beyond, interrogating underlying assumptions and proposing alternatives where appropriate

Data Infrastructure and Expertise

• Develop and maintain deep expertise in Abridge's data assets — including production data, user feedback signals, and clinical conversation data — and serve as the team's authority on data provenance, structure, and limitations for research studies
• Build, extend, and maintain the data pipelines that support internal and external research efforts, working across raw data sources to produce clean, well-documented, research-ready datasets
• Collaborate with Data Engineering and platform teams to ensure that the data the research team needs is accessible, reliable, and well-understood

Cross-Functional Research Collaboration and Communication

• Collaborate closely with product, engineering, science, and data teams to ensure evaluation and analysis are credible, decision-relevant, and grounded in a deep understanding of product development and integration
• Partner with commercial teams, and liaise with customers through relationships owned by our Partner Experience organization, to ensure measurement and evaluation reflect how products are used and experienced in real-world practice
• Translate complex analyses into clear, nuanced narratives grounded in data, tailoring communication to different audiences and contexts
• Produce technical analyses, reports, and presentations that inform product decisions, guide strategy, and contribute to a rigorous evidence base for the real-world impact of ambient AI in healthcare

What You'll Bring

• 8+ years using SQL and Python or R for data science, including experience building or working closely with data pipelines and data infrastructure
• 3+ years of data science experience in academic or industry research settings where you contributed to research studies relying on complex data processing and analysis
• Comfort operating across the spectrum from data engineering to applied science — you can develop data pipelines and also conduct a rigorous evaluation study in collaboration with research scientists
• Experience with code-based data visualization tools (e.g., Seaborn, ggplot2)
• Demonstrated ability to conduct empirical evaluations using observational or experimental data, grounded in a rigorous quantitative or mixed-methods approach
• 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, clinicians, and patients
• Excellent communicator capable of effectively delivering quantitative findings to non-technical stakeholders in a clear and compelling fashion
• A team player, comfortable assisting others across the organization in solving data problems and answering questions with data