Surgical Data Science Collective - Data Scientist
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Requirements
• Master's degree (or equivalent experience) in statistics, biostatistics, data science, computer science, or a related quantitative field • 2+ years of experience in applied data science or quantitative research • Strong Python skills for data analysis and pipeline development (pandas, NumPy, SciPy, scikit-learn) • Solid understanding of statistical methods: regression, hypothesis testing, dimensionality reduction (PCA/factor analysis), bootstrap inference • Experience with SQL databases (PostgreSQL preferred) and NoSQL databases (MongoDB) • Ability to work independently on ambiguous problems — scoping analyses, choosing methods, and communicating trade-offs • Strong written communication — ability to produce clear reports for both technical and non-technical audiences • Experience with Git and collaborative software development practices • Preferred • Experience with healthcare, clinical, or biomedical data • Familiarity with Bayesian methods or mixed-effects models • Experience with cloud infrastructure (AWS — S3, SageMaker, or similar) • Experience building interactive dashboards or data visualization tools • Familiarity with surgical workflow, medical devices, or clinical methodology
Responsibilities
• Study design and execution: Design and run clinical validation studies — correlating AI-derived metrics with surgical outcomes (e.g., complications, resection extent, procedure duration) • Study design and execution: • Scoring methodology: Develop and refine composite scoring algorithms (PCA-weighted, Bayesian, or other approaches) that summarize multi-dimensional surgical performance into interpretable scores • Scoring methodology: • Statistical modeling: Apply appropriate statistical methods (logistic regression, mixed effects, survival analysis, dimensionality reduction) to clinical datasets with clustered, sparse, and heterogeneous data • Statistical modeling: • 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 pipeline development: • Data quality and integrity: Design and implement data validation checks, investigate discrepancies across data sources, and ensure reproducibility of analyses • Data quality and integrity: • Clinical collaboration: Work directly with surgeons and clinical researchers to define metrics, interpret results, and refine tools based on clinical feedback • Clinical collaboration: • Reporting and communication: Produce analysis reports, methodology documentation, and presentations for internal teams, clinical partners, and external stakeholders • Reporting and communication:
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