Clinical Data & ML Research Analyst

Stanford Reglab; Palo Alto (CA); In loco; USD 90.000 - 150.000; Tempo pieno

Descrizione del lavoro

Stanford's Division of Clinical Informatics seeks a Research Data Analyst to contribute to clinically applied machine learning, foundation models, and multicenter studies. You will work with researchers, data scientists, and clinicians to prepare and analyze complex datasets, evaluate model performance, and generate insights for reproducible research.

The role involves building end-to-end analytical pipelines, data quality assessment, cohort development using OMOP CDM, and collaboration across

Competenze

• Experience developing and implementing algorithms for processing large datasets.
• Background in computer science, engineering or a related field with strong statistics and research.
• Excellent project and time management skills with attention to detail.
• Ability to work collaboratively with stakeholders across a large institution.
• Outstanding problem solving and analytical skills; strong writing abilities for grants/papers.
• Proficiency in SQL, Python and shell scripting.

Mansioni

• Collect, manage and clean datasets.
• Interpret, analyze, and visualize multivariate relationships in data.
• Develop algorithms and statistical models; perform analyses for reporting.
• Identify data quality issues and determine root causes.
• Create reports, charts and tables for investigators and publication.
• Collaborate with faculty and research staff on data collection and methods.
• Provide documentation based on audit criteria to investigators.

Conoscenze

SQL

Python

Shell scripting

Statistics

Data visualization

Machine learning concepts

Formazione

Bachelor's degree

Strumenti

SQL

Python

Shell scripting

Descrizione del lavoro

Stanford's Division of Clinical Informatics seeks a Research Data Analyst to contribute to clinically applied machine learning, foundation models, and multicenter studies. You will work with researchers, data scientists, and clinicians to prepare and analyze complex datasets, evaluate model performance, and generate insights for reproducible research.

The role involves building end-to-end analytical pipelines, data quality assessment, cohort development using OMOP CDM, and collaboration across