Data Science Research Assistant: Cardiovascular AI/NLP

Columbia University in the City of New York; New York (NY); On-site; USD 27,000 - 38,000; Part time

Job summary

Columbia University in the City of New York's School of Nursing invites applications for a data science focused student research worker to support AI and ML research in cardiovascular health informatics. You will work with structured and unstructured EHR data under the direction of Dr.

Kang's federally funded program. Responsibilities include preparing and cleaning datasets, ensuring data quality, extracting information from clinical notes, literature reviews, and documenting data management

Qualifications

• Bachelor's degree or equivalent in education and experience.
• Experience with Python and/or R for data science.
• Familiarity with ML libraries and NLP tools.
• Knowledge of statistics and data visualization.
• Ability to work with large datasets and structured/unstructured data.

Responsibilities

• Preparing, cleaning, and organizing research datasets (structured EHR data: vitals, labs, medications, diagnoses).
• Supporting data quality checks and basic data summaries under supervision.
• Assisting with extracting information from clinical notes using existing tools/workflows.
• Supporting literature reviews related to AI/ML and clinical research.
• Documenting data management procedures and study protocols (data dictionaries, IRB/DUA materials).
• Participating in research team meetings and providing task updates.
• This is a grant funded position; continued employment depends on funding.

Skills

Python

R

Machine Learning

NLP

Data Visualization

Data Analysis

Big Data

Analytical Thinking

Education

Bachelor's degree or equivalent

Tools

scikit-learn

TensorFlow

PyTorch

spaCy

NLTK

Hugging Face

Job description

Columbia University in the City of New York's School of Nursing invites applications for a data science focused student research worker to support AI and ML research in cardiovascular health informatics. You will work with structured and unstructured EHR data under the direction of Dr.

Kang's federally funded program. Responsibilities include preparing and cleaning datasets, ensuring data quality, extracting information from clinical notes, literature reviews, and documenting data management