Research Intern at GE HealthCare · WA07-01-B…

Summary: GE HealthCare is seeking undergraduate science major students passionate about applying data science and quantitative methods to advance medicine and healthcare. The role involves analyzing complex biological and clinical datasets, contributing to research, and developing data-driven solutions in healthcare. Responsibilities: - Analyze large-scale biological and healthcare datasets using statistical and computational methods - Apply statistical modeling techniques and, where appropriate, machine learning approaches to support research questions - Contribute to the design and development of machine learning models (including exposure to deep learning and LLM-based approaches) to support automation of clinical or research tasks using data such as medical images, electronic health records, waveforms, and clinical reports - Write and maintain code in R and/or Python for data cleaning, transformation, analysis, and visualization - Perform statistical validation, error estimation, and interpretation of model and analysis results across diverse datasets - Assist in building prototypes and reproducible workflows that support scalable, high-quality research and algorithm development - Collaborate with researchers to translate biological and clinical questions into quantitative analyses and data-driven solutions - Support the exploration and evaluation of new methodologies, tools, and technologies in data science, machine learning, and healthcare applications - Contribute to preparation of research outputs, including visualizations, reports, and potential publications Required Qualifications: - Currently enrolled in a Bachelor's degree in Biology, Biostatistics, Bioinformatics, or a related field - Strong foundation in statistics (e.g., hypothesis testing, regression, data analysis) - Proficiency in R and/or Python for data analysis - Familiarity with at least one area of machine learning concepts such as Computer Vision, Deep Learning, or Algorithmic Foundations of Optimization - Experience working with large datasets and data wrangling techniques - Ability to interpret and communicate quantitative results in a biological or clinical context - Strong problem-solving skills and ability to work under research supervision Preferred Qualifications: - Exposure to machine learning or AI concepts (e.g., classification, clustering, predictive modeling) - Familiarity with biological or clinical data types (e.g., genomics, imaging, EHR data) - Experience with data visualization tools (e.g., ggplot2, matplotlib, seaborn) - Knowledge of reproducible research practices (e.g., version control, scripting workflows) - Interest in contributing to research publications or presentations Required Skills: Statistics, R, Python Important Skills: Machine Learning, Large Datasets Nice-to-Have Skills: Data Visualization, Reproducible Research Benefits: Professional development, Challenging careers, Competitive compensation