Cedars-Sinai is seeking a highly motivated and analytical Data Science Intern to support our data and research initiatives in a fully remote environment. This internship offers a unique opportunity to work with real-world healthcare data while contributing to innovative, data-driven solutions that improve patient outcomes and operational efficiency.
The intern will collaborate with data scientists, clinicians, researchers, and technology teams to analyze complex datasets, develop predictive models, and generate actionable insights. This role is ideal for students or recent graduates passionate about data science, healthcare analytics, and applied research.
Key Responsibilities
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Assist in collecting, cleaning, and preprocessing large healthcare datasets from multiple sources.
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Perform exploratory data analysis to identify trends, patterns, and anomalies.
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Support the development and validation of statistical models and machine learning algorithms.
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Create data visualizations, dashboards, and reports to communicate findings effectively.
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Collaborate with cross-functional teams including clinical, research, and IT departments.
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Document methodologies, code, and analytical processes to ensure reproducibility.
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Participate in team meetings, code reviews, and knowledge-sharing sessions.
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Adhere to data privacy, security, and compliance standards, including HIPAA guidelines.
Required Skills and Qualifications
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Currently pursuing or recently completed a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Biomedical Informatics, or a related field.
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Strong foundation in statistics, data analysis, and problem-solving.
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Proficiency in Python or R for data analysis and modeling.
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Familiarity with data manipulation libraries such as Pandas, NumPy, or similar.
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Basic knowledge of SQL and relational databases.
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Experience with data visualization tools such as Matplotlib, Seaborn, Tableau, or Power BI is a plus.
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Strong written and verbal communication skills.
Experience
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Prior internship, academic project, or research experience in data science or analytics is preferred but not required.
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Experience working with healthcare or clinical data is an advantage, but not mandatory.
Working Hours
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Internship position (Part-time or Full-time, depending on program requirements)
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Fully remote work environment
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Flexible working hours aligned with academic commitments and team needs
Knowledge, Skills, and Abilities
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Understanding of basic machine learning concepts and statistical modeling techniques.
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Ability to translate complex data into clear, actionable insights.
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High level of attention to detail and data accuracy.
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Ability to manage time effectively and meet project deadlines.
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Strong collaboration skills and ability to work in a multidisciplinary environment.
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Curiosity, adaptability, and eagerness to learn in a fast-paced research setting.
Benefits
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Hands-on experience with real-world healthcare data and advanced analytics tools.
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Mentorship from experienced data scientists, researchers, and healthcare professionals.
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Exposure to cutting-edge healthcare research and innovation.
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Flexible remote work arrangement.
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Opportunity to build a strong professional network within a leading healthcare institution.
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Potential academic credit (subject to institution approval).
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Competitive internship stipend (where applicable).
Why Join Cedars-Sinai
Cedars-Sinai is internationally recognized for excellence in patient care, research, and medical innovation. Joining our team as a Data Science Intern allows you to contribute to meaningful work that directly impacts healthcare delivery and patient outcomes. You’ll gain invaluable experience at the intersection of data science and medicine while working in a supportive, mission-driven environment.
How to Apply
Interested candidates should apply through the official Cedars-Sinai Careers portal. Please submit your updated resume, a brief cover letter outlining your interest in data science and healthcare, and any relevant academic or project work. Selected candidates will be contacted for further evaluation.