At LifeStance Health, the business problem is to improve mental health care quality through quantitative research and data analysis, leveraging national data to generate novel research findings and translate them into tangible improvements in care quality.
Requirements
- Master’s degree in a quantitative discipline such as applied statistics, mathematics, biostatistics, data science, epidemiology, health economics and outcomes research, or a related discipline with 10+ years full-time experience conducting quantitative health care research in provider, payor, consulting, or other industry settings, required.
- 5+ years using open-source programming languages (Python or R) for data cleaning, statistical analyses, data visualization, and generating summary reports, required
- Multiple years of experience conducting statistical analyses on real-world clinical data, such as electronic health records, claims databases, observational cohort studies, or clinical trials
- Strong quantitative skills in statistical analysis of longitudinal and observational data, with mastery of techniques for related study designs (missing data, propensity score weighting, etc.)
- Intermediate SQL skills, with 2+ years of experience using SQL to extract and transform extract data from cloud data warehouse
- Applied experience in core principles of machine learning (e.g., cross-validation, model tuning) and building predictive models for clinical use cases
- Familiarity with AWS tech stack (e.g., Redshift, EC2, S3, etc.)
Responsibilities
- Serve as expert analyst for clinical quantitative research, owning the planning and execution of quantitative analysis to support all clinical research at LifeStance.
- Leverage our national data to generate novel research findings on the effectiveness of a broad range of outpatient treatments (e.g., psychotherapy, medication, TMS) across virtual and in-person settings
- Implement a variety of statistical approaches tailored for complex research questions, such as longitudinal analysis, survival modeling, multi-level modeling, and predictive modeling.
- Extract and transform electronic health record data into structured datasets suitable for research analysis. Partner with other analysts and data engineering to define reproducible, versioned data assets.
- Provide research findings to support validation and dissemination of high-quality mental health care through peer-reviewed research, conference presentations, white papers, and blog posts
- Conduct quantitative analysis to support design of evidence-based quality improvement initiatives (e.g., clinical care pathways, patient-clinician matching, clinical decision support)
- Explore opportunities to embed predictive analysis and predictive models into clinical and operational workflows to improve care quality and patient retention
Other
- Master’s degree or PhD in a quantitative discipline
- 10+ years full-time experience conducting quantitative health care research in provider, payor, consulting, or other industry settings
- Demonstrates awareness, inclusivity, sensitivity, humility, and experience in working with individuals from diverse ethnic backgrounds, socioeconomic statuses, sexual orientations, gender identities, and other various aspects of culture.
- Strong collaboration and communication skills, with clear record of independent stakeholder partnership to iterate on results and align on goals
- Qualified candidates must be legally authorized to be employed in the United States.
