United States onlyWe are looking for a Data Scientist to lead analytical workstreams across our surgical analytics platform. You will design and execute studies that validate AI-derived surgical metrics against clinical outcomes, develop composite scoring methodologies, and build data pipelines that scale across procedure types and clinical sites.
Requirements
- Study design and execution: Design and run clinical validation studies — correlating AI-derived metrics with surgical outcomes (e.g., complications, resection extent, procedure duration)
- Scoring methodology: Develop and refine composite scoring algorithms (PCA-weighted, Bayesian, or other approaches) that summarize multi-dimensional surgical performance into interpretable scores
- Statistical modeling: Apply appropriate statistical methods (logistic regression, mixed effects, survival analysis, dimensionality reduction) to clinical datasets with clustered, sparse, and heterogeneous data
- Data pipeline development: Build and maintain Python pipelines that extract, transform, and analyze data from MongoDB, PostgreSQL, and S3 at scale (hundreds to thousands of procedures)
- Data quality and integrity: Design and implement data validation checks, investigate discrepancies across data sources, and ensure reproducibility of analyses
- Clinical collaboration: Work directly with surgeons and clinical researchers to define metrics, interpret results, and refine tools based on clinical feedback
- Reporting and communication: Produce analysis reports, methodology documentation, and presentations for internal teams, clinical partners, and external stakeholders
